Method and apparatus for determining inventor impact

ABSTRACT

A method for determining intellectual property value, includes creating a source of patent data; and analyzing the data to determine a patent value. Also disclosed is an instruction set executable on a machine that includes a processor and a memory. The instruction set executable on the machine to cause to the machine to: create a source of patent data; and to analyze the data to determine a patent value. A media carrying an instruction set executable on a machine. The machine includes a processor, and a memory associated with the processor. The instruction set is executable on the processor to cause to the machine to: create a source of patent data; and analyze the data to determine a patent value.

PRIORITY CLAIM

This application claims benefit of U.S. application Ser. No. 16/110,848,filed on Aug. 23, 2018, which claims benefit of U.S. ProvisionalApplication Nos. 62/549,268, filed on Aug. 23, 2017, and 62/638,699,filed on Mar. 5, 2018, and which applications are incorporated herein byreference. A claim of priority is made.

BACKGROUND OF THE INVENTION

Fixed assets are physical items that an organization owns, whichincludes machinery, infrastructure, and land. Intangible assets arenon-physical items, including patents, trademarks, goodwill andcopyrights. Intangible assets are larger than tangible assets for agrowing range of organizations. Patents are a highly visible and welldocumented part of intangible assets, and valuing an organization'spatents is an important direct and indirect measure of an organization'scurrent value, and their ability to grow.

Unfortunately, patents are difficult to value. One approach toevaluating a group of patents is performing an expert assessment of thepatents, their related art, and their prosecution history. Expertassessment of patent value is expensive and time-consuming, and is onlyused for a limited number of patents. Most patent valuations are verysubjective and are based on several approaches which can vary widely inaccuracy.

In some instances, simple metrics are used to aid in valuation of apatent or patent portfolio. Simple metrics have limited utility, sincemany of these metrics are lagging or indirect measures, or both. Anotherchallenge with using simple metrics for patent value is that some of thekey metrics can be easily manipulated. Inventors and companies canmanipulate the data by improperly citing their own patents in cases. Lawfirms may do the same and then tout that they write stronger patentsbased on the number of citations to patents they have written when infact they may use a software package to cite every patent within afamily to new members of the family or to newly drafted cases. Forexample, it is possible for an inventor or group of inventors to focuson generating many patents, and to increase forward citations throughself-citations. It is less likely that these patents would have as highof an average value than a well-culled set of inventions.

What is needed is a timely, quick, and accurate method of assessing thevalue of patents. Furthermore, a method is needed for assessing thecontributions and likely future contributions of both individual andgroups of inventors.

SUMMARY OF THE INVENTION

A method and an apparatus for deducing patent value through analysis ofinventor capabilities. In addition, a method and an apparatus forevaluating inventors and groups of inventors capabilities inconsistently producing high impact inventions.

A method and an apparatus for determining inventor impact includesremoving inventors and inventions which are duplicative from one or moredatabases. These databases are then used to numerically depict certainaspects of inventors in the inventive community. The data can then beused to identify individual inventors to certain groupings of inventors.The value of patents can be determined from the standpoint of individualinventors, or from the standpoint of a company, or from a combinationthereof. The data can even be used to select individuals for building aninventive team.

A process for determining intellectual property value which includes:providing a source of patent data, analysing at least theclassifications and inventor order of the inventors, and summarizing theanalysis into at least one innovation metric for the inventors. In oneembodiment, the at least one metric analysis further includes the domainexperience of the inventors. In still another embodiment, the method isused to evaluate a group of at least 10 patents. In yet anotherembodiment, the innovation characteristics are determined for allinventors. In another embodiment, a group of patents is evaluated andthe change in the evaluation is tracked in time.

Also included is a process for determining intellectual property valuewhich includes: providing a source of patent data, analysing at leastthe classifications and inventor order of the inventors, and summarizingthe analysis into at least one innovation metric for the inventors,wherein the at least one metric further includes the inventor network.In one embodiment the patents are related by a common classification,e.g. an IPC. (International Patent Classification), or the patents arerelated by a common inventor, or the patents are related by a commonassignee. In still a further embodiment, the innovation metric is basedon a weighting of different contributions of the innovation metric,where the weighting is based on the inventor order for each patent. Inyet another embodiment, the patents are valued within a patent class andthis valuation is compared between assignees. In still anotherembodiment, the patents are valued within a patent class and thisvaluation is compared between a first organization and a secondorganization for the purposes of assessing an acquisition value.

Another method for valuation of at least one patent includes:identifying the inventors on a patent, determining an innovationcharacteristic for at least one of the inventors, and creating avaluation metric that is based on the innovation characteristic.

Still another invention is an innovation characteristic that depends onfactors selected from the group of the co-inventors, the number ofpatent classifications, the forward citations from the inventors of anexisting portfolio. Also included in the innovation characteristics is adepth of classifications, a breadth of classifications, a number ofcountries filed in, back citations, self-citations, recognition,financial impact, an average position in an inventor list, membership ina collaborator network, a number of inventors per patent, a number ofexisting patents, a number of existing patent families, and an inventornetwork role. In one embodiment, the above listed characteristics areused to identify innovators with high probability of creating high valueinventions. In another embodiment, the above characteristics are used toidentify innovators with high probability of creating high valueinventions.

A method for evaluating the innovation capability of an inventorcomprising an array of inventor characteristics for at least a first andsecond inventor, an assessment of at least the first inventor and usingthe assessment to train a computer to predict the inventorcharacteristics of the second inventor.

Many of the above methods include programming a processor of a machineto determine exact values. One apparatus is a machine with a processorthat performs at least a part of the methods set forth above. Anotherapparatus can be an instruction set on a media. The instruction set,when executed by the processor will perform at least part of one or moremethods discussed above.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a flow chart of steps and results for assessing individualinventors, according to a preferred embodiment.

FIG. 2 is a chart of the trend in the average number of forwardcitations per patent (vertical axis) for all 228 organizations for theyears (horizontal axis) 1970 to 2013.

FIG. 3 is another plot of the average forward citations (vertical axis)vs. year (horizontal axis) that also includes a curve for predicting thelong term average forward citations for future years, according to anexample embodiment.

FIG. 4 is another plot of the average forward citations (vertical axis)vs. year (horizontal axis) that also includes a curve of average forwardcitations for inventors categorized as specialists, architects, andinnovators (SAI), according to an example embodiment.

FIG. 5 is another plot of the average forward citations (vertical axis)vs. year (horizontal axis) that also includes normalized curves ofaverage forward citations for inventors categorized as specialists,architects, and innovators (SAI), according to an example embodiment.

FIG. 6 is plot of the average forward citations (vertical axis) vs. year(horizontal axis) that also includes normalized curves of averageforward citations for inventors categorized as specialists, architects,and innovators (SAI), and which correlates company events to thesenormalized curves, according to an example embodiment.

FIG. 7 is plot of an innovator score (vertical axis) vs. the number ofinventors in an organization (horizontal axis) for three organizations,according to an example embodiment.

FIG. 8 is plot of a sum of Innovator scores (vertical axis) for anorganization's inventors and is plotted against the sum of Architectscores (horizontal axis) for six organizations with patents in aspecific international patent class (IPC) field, according to an exampleembodiment.

FIG. 9A is plot of a normalized sum of Innovator scores (vertical axis)for a first organization against the normalized sum of Architect scores(horizontal axis) for an organization, according to an exampleembodiment.

FIG. 9B is plot of a normalized sum of Innovator scores (vertical axis)for a second organization against the normalized sum of Architect scores(horizontal axis) for an organization, according to an exampleembodiment.

FIG. 10 is plot of a change in an Innovation metric (vertical axis) foran organization over a selected time period against the innovationmetric over the time period (horizontal axis) for an organization,according to an example embodiment.

FIG. 11 is plot of a T-value (Tv) number (vertical axis) plotted againstthe inventor number (horizontal axis) for a plurality of organizations,according to an example embodiment.

FIG. 12 is a plot of a normalized innovation score against the date.Normalized average Architect scores, Innovator scores, and Specialistscores are plotted against the organization's enterprise value and theorganization's market cap, according to an example embodiment.

FIGS. 13A, 13B, 13C, and 13D are charts with each chart showing thenumber of forward citations for an inventor against the dates, accordingto an example embodiment. Charts for four inventors with differentorganizations are shown in FIGS. 13A, 13B, 13C, and 13D are charts witheach chart.

FIG. 14 is chart plotting the Architect-Innovator scores (horizontalaxis) against the change in stock price (vertical axis) over a timeframe, according to an example embodiment.

FIG. 15 is chart plotting the Architect-Innovator scores (horizontalaxis) against the change in stock price (vertical axis) over anothertime frame, according to an example embodiment.

FIG. 16 is chart plotting the slope and r² for a linear fit to the chartof FIG. 14 (vertical axis) against the investment date (vertical axis),according to an example embodiment.

FIG. 17 is a schematic view of a computer system that can be operated inaccordance with the n example embodiments discussed herein.

FIG. 18 is a diagram showing the commands between an inventor and atleast an application user interface, and different queries, according toan example embodiment.

FIG. 19 is a schematic of a computer system that includes memory and aprocessor, according to an example embodiment. The computing systemshown interacts and uses data from a number of public and privatedatabases including and internet data.

FIG. 20 is a schematic of a set of databases that are operated on by aprocessing system that executes an instruction set to reorganize,disambiguate and condition data received from one or more databases,according to an example embodiment.

FIG. 21 is a schematic of a set of databases that are operated on by aprocessing system that executes an instruction set to reorganize,disambiguate and condition data received from one or more databases, andfurther includes financial data and a machine learning training set andsubsystem, according to an example embodiment.

FIG. 22 is a schematic of a set of databases that are operated on by aprocessing system that executes an instruction set to reorganize,disambiguate and condition data received from one or more databases, andfurther includes financial data and a supervised machine learningtraining set and subsystem, according to an example embodiment.

FIG. 23 is a schematic of a set of databases that are operated on by aprocessing system that executes an instruction set to reorganize,disambiguate and condition data received from one or more databases, andfurther includes financial data and an unsupervised machine learningtraining set and subsystem, according to an example embodiment.

FIG. 24 is a flow diagram of a method of the invention, according to anexample embodiment.

FIG. 25 is a flow chart of a method for determining patent value,according to an example embodiment.

FIG. 26 is a flow chart of a method for ranking and identifying orclassifying inventors, according to an example embodiment.

FIG. 27 is a flow chart of a method for comparing groups of inventors,according to an example embodiment.

FIG. 28 is a flow chart of a method for predicting forward citation of apatent document, according to an example embodiment.

FIG. 29 is a flow chart of a method for predicting forward citation of apatent document, according to an example embodiment.

FIG. 30 is a flow chart of a method, according to an example embodiment.

FIG. 31 is a flow chart of another method, according to an exampleembodiment.

FIG. 32 is a computer system for predicting forward citations for aninventor, according to an example embodiment.

FIG. 33 is a computer system for predicting forward citations for apatent, according to an example embodiment.

FIG. 34 is a computer system for predicting the value of the patentbased on the activity of a plurality of inventors, according to anexample embodiment.

FIG. 35 is a computer system for predicting the value of the patentbased on the activity of a plurality of inventors, according to anexample embodiment.

FIG. 36 is a computer system for comparing the amount of innovation fortwo or more organizations, according to an example embodiment.

FIG. 37 is a flow chart of a method for identifying inventors or othersin an organization, according to an example embodiment.

FIG. 38 is a schematic diagram of another representation of a computingdevice for a machine in the example electronic form of a computersystem, according to yet another embodiment.

DETAILED DESCRIPTION OF THE INVENTION

There are many approaches for patent valuation. One is to analysemetrics of the patent. These metrics commonly include citations,particularly forward and backward citations, the number of claims, thelength of claims, the age of the patent, the size of the patent familyand related portfolio, and the number of international filings. Of thesemetrics, forward citations are generally considered to be the bestindicator of patent value (5, 6). Forward citations, though, are alagging indicator of patent value (6).

Patent data from data providers commonly provides bibliographic data,citation data, and legal data. Bibliographic data includes the patenttitle, key dates, foreign equivalents, inventors and relatedinformation, and assignees and related information. Citation data iscommonly provided as backward citations. Forward and self-citations canbe determined from analysis of other patents, publications, andliterature. Legal data includes prosecution history, office actions,continuations, assignment transfers, and the status of patents.

Patent data typically becomes publicly available 18 months after filing.The information that is available through the publication of theapplication, bibliographical data, and legal data, includes patentclassification areas that relate to the application as well as atypically small set of cited patents. The number of cited patents willincrease during patent prosecution due to additions from the inventors,through Information disclosure statements or their equivalent, andrelated art found by the examiner. These cited patents are a combinationof backward citations and self-citations (inventors citing their ownpreviously filed art). To a small degree before publication, and morerapidly after publication, other patents will cite the patent creatingforward citations. Forward citations typically become a statisticallysignificant metric between 4-7 years after the patent publishes, andmost of a typical patent's forward citations accrue 10-15 years afterpublication.

In some instances, the metrics for patent value can be weighted, and theresulting number is used to assess the value of a patent. For example:

-   -   1, Forward citations (40%)    -   2. Age of patent from priority date (20%)    -   3. Independent claim count (adjusted by number of means claims)        (15%)    -   4. Claim 1 word count (15%)    -   5. Family size and international filings (10%)

As discussed above, forward citations are a lagging indicator.Independent claim count, claim 1 word count, and family size andinternational filings are also all lagging metrics, since all thesemetrics may change during prosecution, continuations, and national stagefiling.

An alternative approach to measure patent value for an organization oran individual inventor is simply measure the number of patents or patentfamilies (8). This approach has the advantage of a short lag time and isrelatively easy to apply, but the power-law distribution of patent valuemeans that this analytic can have a very large error. The power-lawdistribution means that most filed patents have little to no value, anda small number of patents can dominate a patent portfolio's value.Individual inventors may be assessed by their forward citations, but asfor patents, the number of forward citations is a lagging indicator.Individual inventors may also be assessed through their co-inventornetwork, but this is commonly a simple graphical assessment of nodes andedges of the network, or the count of different co-inventors. These dataare commonly used as a qualitative assessment of connections, ratherthan as an accurate tool for assessing individual inventors. Assessingthe value contributed by individual inventors is important forrecruiting, compensation, engagement, and organizational structure. Theuse of statistically noisy and lagging indicators in assessing inventorsdoes not work well at an individual level.

The value of an inventor's patents can be correlated to their depth andbreadth of Patent Classifications at a classification level, and thenumber of co-inventors. The results from the study, as is the case forstatistical assessment of patents, is found to be statistically noisy,and was imprecise in assessing individual patents and their inventors.

The invention uses patent data, where the data at least has inventornames, at least one key patent date, key dates being the earliestpriority date, publication date, and the like, and the inventor'sco-inventor names. Preferably, data on the order of the inventor's namesas provided on the patent or patent application, or both, is available.Preferably, the data also contains patent classifications, for example,International Patent Classifications (IPC) and the Cooperative PatentClassification (CPC). Preferably, the patent data also contains citationdata, at least containing backward citations, from which, forward andself-citations may be calculated. Citation data may also includeexaminer assessments for cited art, such as X, Y, or A designation.Preferably, the data contains legal data, including office actions,inventor responses, assignments, and if the patent or application isalive. Furthermore, the data preferably contains other sources ofinformation on the inventors, including, for example, non-patent relatedpublications and information. Additional inventor data may includecurrent employer, network, and skills. Preferably, the data alsoincludes the law firm working with the inventor.

Patent specifications and claims may also be used to assess inventors.The depth of discussion on technical attributes, examples, simulationresults, experimental measurements support inventor depth. Additionally,a wide variation on solutions that enable the invention and problemsthat the solution can solve support inventor breadth. The relationshipbetween patents that make up an inventor's patent portfolio can alsosupport inventor depth, or breadth, or both, and may also indicate aninventor's ability to solve a complex system problem in protecting andexploiting a new technology. The number and types of claims can alsoindicate inventor capabilities.

Other sources of data include assessments on the characteristics,competencies, and capabilities of all or a group of inventors. Theassessments are preferably based on expert opinion or by a consensus ofmultiple individuals. Other information may include professionalrecognition such as awards, degrees, and rank, and role within anorganization. The assessments may include, but are not limited to,assessment of the inventor's prior patents. The assessment may include,patent scope, validity, discoverability, and portfolio reinforcement,economic value and impact, and technological impact. Economic impact maybe estimated through one or a combination of audits, opinion, patentoutcomes measured by either entering or conclusions from litigation,continued payment of maintenance fees, licensing, enabled product andservice sales, and number of international filings. The assessment mayalso include judgement of the political skill of the inventor, wherepolitical skill may be assessed for either promoting the patentedtechnology for common good or for self-interest. The inventor may beassessed for the role that chance contributed to their success, and theprobability of future success. The inventor may be assessed for how theywork within teams, specifically if they work better independently, aspart of a homogeneous group, or as part of a more complex and dynamiccollaboration.

At least a portion of the inventors may be assessed for theircapabilities relative to other inventors as a Specialist, an inventorwith narrow and deep inventor domain experience, a Generalist, aninventor with broad and shallow inventor domain experience, or aSpecialist-Generalist, an inventor with both broad and deep inventordomain experience. A portion of inventors may be assessed as InventiveLeaders; one who proposes and drives a patent application preparation,or an Enabler; one that supports other inventors with, for example,access to skills, networks, and capabilities. At least a portion of theinventors may be characterized by the ability to make unusualcombinations of materials, processes, articles, organizationalcapabilities, and needs. At least a portion of the inventors may beranked by their ability to successfully develop solutions forcomplicated problems. Complicated problems are defined as where theproblem and solution-space are well-defined, and there is neithersubstantial interactions nor dependency on necessary changes in the restof the organization, partners, competitors, and customers orcombinations thereof. At least a portion of the inventors may also beranked by their ability to successfully develop solutions for complexproblems. Complex problems are defined as where the problem andsolution-space are poorly or insufficiently defined, and there aresubstantial interactions and dependency of the rest of the organization,partners, competitors, and customers or combinations thereof.

Specialists have deep expertise and work effectively on complicatedproblems, particularly by advancing the art. Architects have deep andbroad expertise, and work effectively on complicated problems,particularly by creating novel combinations of existing art. Innovatorshave deep and broad expertise, and work effectively on complex problemsby a combination of advancing the art, using new combinations ofexisting and new art, and solving system problems as needed.

The terms “Specialist”, “Architect”, and “Innovator” are intended to begeneral labels describing inventor capabilities, and is not meant to belimiting. For example, a single inventor may have differing levels ofcapabilities of all three, and may have other capabilities as well.

FIG. 1 which is discussed in detail below, is one embodiment of a flowchart that provides a roadmap for the more detailed discussion of theinvention.

A significant problem with currently available patent data is theconsistency of an inventor's and assignee's name across multiplepatents. Recently, there has been a great deal of effort placed ondisambiguating inventor's and assignee's names. The most common approachfor inventor disambiguation is to look at names that are similar, forexample, Kate Olson and Katherine P Olson, and decide if they are thesame person by looking at a combination of geographic data, commonassignees, common areas of technology (classifications), or commonco-inventors, and combinations thereof. This is a highly demandingcomputation, and has only recently become possible to process for alarge number of patents.

While the current disambiguation programs are effective in combiningnames, this creates a problem for assessing inventors as described inthis invention. The current disambiguation programs make a large numberof false-positive errors, i.e., incorrectly attributing patents to oneperson, but are effective at minimizing false-negative errors, where asingle inventor's patents are split between two or more inventorentities in the database, or miss-attributed to another inventor. Whilethis is not a major issue in assessing patent value directly, it is asevere issue in assessing inventors. We are assessing inventive recordsfor every inventor, and these incorrectly joined inventors substantiallydistort the data. For example, two inventor's patent records may becombined and attributed to a single person. There are a very largenumber of these overly combined inventors in commercially availabledisambiguated databases, and these overly combined inventors (falselyprolific inventors) can be approximately equal to the number of recordsof correctly disambiguated inventors (truly prolific inventors).Unfortunately, Bayesian classification by the inventor's number ofpatents was not useful due to the overlap between truly and falselyprolific inventors. Surprisingly, a solution was found by consideringthe number of organizations that the inventor belongs to and the numberof assignee changes an inventor had to date, among other data, inclassifying the overly combined data. For example, bibliographic datafor a true inventor would show relatively few organizational changesbased on the assignee for the patent, whereas a false inventor (made upfrom data from several inventors) would show a large number oforganization changes based on bibliographic data. For westernorganizations in PATSTAT patent data, this reduced the number offalse-positive errors from an unacceptable 35% of prolific inventors toless than 3%, and false-negative errors did not change.

Preferably, the inventor database containing more than 1000 inventors,more than 10,000, or more than 1,000,000 inventors, has no more than 30%of the prolific inventors being falsely prolific inventors, preferablyno more than 15%, and most preferably more than 5%. Prolific inventorsare defined here as those with more than 10 patents, alternatively morethan 25 patents, alternatively more than 50 patents.

Another method for solving the disambiguation problem for inventors andassignees is to use supervised machine learning using the data generatedfrom this invention. For example, patent data can be provided to acomputer, where the names of the inventors and assignees are notdisambiguated, or are only partially disambiguated. The program runningon the computer can determine inventor characteristics, and based onsimilar names, common characteristics, and related data, can improvedisambiguation.

The patent data may be conditioned before analysing inventors.Conditioning may include reorganizing the patent data so it is organizedby each inventor rather than each patent. An example of the commonformat for organizing patent data, which is by patent ID, is shown intable 1.

TABLE 1 Patent Inventor Backward ID Date IDs Assignee IPCs Citations 100D1 A, B, C AA B03C 1/02, 80, 83 G02F 1/017 101 D2 A, D BB G02F 1/015,66, 83, 100 G03F 1/22

Table 1 shows a subset of the typical patent data that is available, andit will be used to illustrate the subsequent processing steps. Eachlabel within a cell of table 1 represent either the patent number (e.g.patent number 100), the date for the patent (e.g. D1 is the earliestpriority date for patent 100), the inventor name (e.g. inventor A), anassignee name (e.g. assignee AA), classifications for patent 100, andpatent numbers cited by the patent (e.g. patent numbers 80 and 83 arecited by patent 100. Table 1 is a patent-centric perspective of thepatent data, where the data is organized by patent application.

TABLE 2 Inventor Patent Inventor Depth Depth Depth Depth Depth ID IDDate Assignee Order 1 2 3 4 5 A 100 D1 AA 1 1 1 1 1 1 A 101 D2 BB 1 1 22 2 2 B 100 D1 AA 2 1 1 1 1 1 C 100 D1 AA 3 1 1 1 1 1 D 101 D2 BB 2 1 11 1 1 Inventor Breadth Breadth Breadth Breadth Breadth Backward ForwardSelf- Co- ID 1 2 3 4 5 Citations Citations Citations inventors A 2 2 2 22 2 0 0 2 A 2 2 2 3 4 3 1 1 3 B 2 2 2 2 2 2 1 0 2 C 2 2 2 2 2 2 1 0 2 D1 1 1 1 2 3 0 1 1

Table 2 shows the patent data of table 1 converted to aninventor-centered perspective. The patent data is conditioned to showthe cumulative effects of each inventor adding patents. The cumulativeeffects include depth and breadth and unique co-inventors.

Depth and breadth is calculated in an improved method of what isdescribed in reference 7. The method of determining depth and breadth isimproved by using weighting factors with multiple levels ofclassifications rather than categorical variables generated from asingle level.

A method of determining inventor depth is shown in Equations 1, 2, and3.

$\begin{matrix}{y_{l_{n}} = {\sum\limits_{m = 1}^{M}\left( \frac{1}{\left( {{inventor}\mspace{14mu}{order}} \right)^{kd}} \right)}} & {{Eq}.\mspace{14mu} 1}\end{matrix}$

Where the array y_(l) _(n) is the sum of the number of occurrences ofeach of the classification codes for the inventor's patents forclassification level l, where each occurrence is weighted by theinventor order. M is the number of occurrences of classification codeswithin an inventor's patent records. kd is a scalar for the impact ofinventor order on depth.

For example, assume that an inventor has two patents or applications,and is the first listed inventor for a patent having G02F as athird-level classification, and the inventor is the second listedinventor for a second patent, also having classification G02F. Ifkd=0.5, then y₃ ₁ ≈1.707.

References to patent records as described in this invention may includeboth issued patents and published patent applications.

l is the patent classification level, where l=1 is the highest level ofa hierarchical classification system, l=2 is the next level, etc. Forexample, for IPCs, Level 1 may be G, or Physics, Level 2 may be G02, oroptics. N is the maximum number of unique classification codes for thelevel l.

For each level for all the inventor's patents, the commonclassifications are counted while being weighted by inventor order. Inother words, classifications in patents where the inventor is one of thefirst listed of multiple inventors will have more significant impact.

The array y_(l) _(n) is then sorted and relabeled such that y_(l) ₁ isthe largest value, y_(l) ₂ is the next largest, etc.Depth can be calculated with Equation 2.

$\begin{matrix}{{Depth}_{l} = {\sum\limits_{n = 1}^{N}{y_{l_{n}}^{Bd}\left( {n^{Ad} - \left( {n - 1} \right)^{Ad}} \right)}}} & {{Eq}.\mspace{14mu} 2}\end{matrix}$

Total depth can be calculated by the weighted sum of each of theDepth_(l) values as shown in Equation 3.

$\begin{matrix}{{Depth}{= {\sum\limits_{l = 1}^{L}\left( {WtD_{l} \times {Depth}_{l}} \right)}}} & {{Eq}.\mspace{14mu} 3}\end{matrix}$

Where WtD_(l) is an array of weighting factors.Breadth may be calculated in a similar method as Depth.

$\begin{matrix}{{Breadth_{l}} = {\sum\limits_{n = 1}^{N}{y_{l_{n}}^{Bb}\left( {n^{Ab} - \left( {n - 1} \right)^{Ab}} \right)}}} & {{Eq}.\mspace{14mu} 4} \\{{Breadth} = {\sum\limits_{l = 1}^{L}\left( {WtB_{l} \times Breadth_{l}} \right)}} & {{Eq}.\mspace{14mu} 5}\end{matrix}$

Where the coefficients for calculating Depth and Breadth are shown inTable 2a below.

TABLE 2a Parameter Value Ad 0.5 Bd 0.5 Ab 1.5 Bb 0.5 kd 1 kb 1Table 2b shows the weighting factors WtD and WtB.

TABLE 2b Classification level l WtD WtB l = 1 (broadest) 0.5 1 l = 2 1.5 l = 3 1 .5 l = 4 1.1 .2 l = 5 (narrowest) 1 .5 .2

Equations 1-5 show one way of calculating the depth and breadth ofskills of an inventor. Other means include manually assessing patents,particularly as part of developing a training set for a supervisedmachine learning algorithm. Unsupervised machine learning may also beused to calculate depth and breadth from patent or inventive outcomes.Patent and inventive outcomes include, for example, organizationalgrowth, profit margins, patent citations, prolific inventor retentionwithin an organization, inventor productivity, market capitalization, orstock price.

Inventor depth and breadth may be measured in other ways, such as byevaluation of their skills and capabilities, or by measuring theinventor's brain with, for example, functional magnetic resonanceimaging (fMRI). fMRI has shown that the depth and creativity can both bemeasured. (Maguire, E. A. et al. London taxi drivers and bus drivers: astructural MRI and neuropsychological analysis, Hippocampus 2006,16(12): 1091-101 and De Pisapia, N., et al. Brain networks for visualcreativity: a functional connectivity study of planning a visualartwork, Nature Scientific Reports 6, 39185, 2016.) fMRI and otherneuropsychological analyses can be used to first correlate the resultsof the analyses with inventor skill, and then repeat the analyses with alarger population of inventors to create a training set.

Depth and breadth may increase linearly as classifications are added toan inventor's records, or they may change non-linearly. For example, theeffect of additional classification instances for an inventor may yielda diminishing impact on depth, whereas it may yield an increasing impacton breadth.

The data also shows non-cumulative information for each patentassociated with each inventor. Non-cumulative data includes inventororder, backward citations, forward citations, and self-citations.

This invention advances the art by developing multiple hierarchicallevels for both depth and breadth metrics. This breakdown into multiplelevels allows improved analysis of an inventor's characteristics andcapabilities.

TABLE 3 Backward Forward Self- Inventor Patent Average Average T-Citation Citation citation ID ID Depth Breadth Value Score Score ScoreSpecialist Architect Innovator Network A 100 A 101 B 100 C 100 D 101

Table 3 shows the inventor perspective of Table 2 further processed toshow inventor characteristics and capabilities (values not shown). Theseinclude:

Average depth. This may be a numerical average of the 5 levels of depth,or it may be a weighted average, or it may be a subset of the levels ofdepth (eg. only level 3). The average weighting may also change based onthe specific inventor characteristic and capability being considered.

-   -   Average Breadth. This may be a numerical average of the 5 levels        of breadth, or it may be a weighted average, or it may be a        subset of the levels of depth (e.g. only level 3). The average        weighting may also change based on the specific inventor        characteristic and capability being considered.    -   T-value. T-value reflects a combination of depth and breadth        metrics. T-value is intended to reflect the        specialist-generalist characteristics of an inventor.    -   Backward citation score. This is a prediction of the ultimate        number of backward citations for a patent considering the        inventor's history, the inventor's co-inventor's history, and a        weighting based on inventor order.    -   Forward citation score. This is a prediction of the ultimate        number of forward citations for a patent considering the        inventor's history, the inventor's co-inventor's history, and a        weighting based on inventor order.    -   Self-citation score. This is a prediction of the ultimate number        of self-citations for a patent considering the inventor's        history, the inventor's co-inventor's history, and a weighting        based on inventor order.    -   Specialist score. An estimation of the inventor's        characteristics and capabilities to advance or apply the art of        a field, and to solve complicated problems. Specialists have        high depth, low breadth, and co-inventor networks with similar        backgrounds to the inventor.    -   Architect score. An estimation of the inventor's characteristics        and capabilities to combine disparate technologies and processes        to solve complicated problems. Architects have high depth, high        breadth, and broad networks of inventors with different areas of        skill than the inventor.    -   Innovator score. An estimation of the inventor's characteristics        and capabilities to solve complex problems. Innovators have high        depth, high breadth, particularly at IPC level 2 and 3, and very        extensive and dynamic networks.    -   Network. An estimation of the value of an inventor's network        measured by one or more of the following:        -   Importance—who collaborated and referenced the inventor, and            what were their characteristics and capabilities?        -   Symmetry—bidirectional references and collaborations.        -   Centrality—number of steps of collaboration to whole network        -   Clustering—Number of triads of collaboration where the            inventors is bidirectional        -   Bridging—number of high quality connections (specialists,            architects, and innovators)        -   Dynamic—how quickly are new connections established when            there are changes for the inventor that affect their            existing network.    -   The network data may be either a single metric based on a        weighted compilation of the individual network analyses, or may        be reflected through multiple scores.

Table 4 shows an example of parameters (values not shown) based on usinga combination of experts or consensus or both to evaluate a portion ofinventors as a training set for supervised machine learning. Forexample, a training set for calculating political skill, economicimpact, architecting, serial innovator, Specialist Score, ArchitectScore, Innovator Score, and Network Score may be generated by expertreview of inventors' performance in each area. Preferably, the inventorsmaking up the training set are highly diverse in skills andcapabilities, and also preferably, the training set has at least 50inventors, more preferably at least 100 inventors. Preferably, thescores follow a power-law distribution, where some exceptional inventorsmay have a 10 or even 100 times or higher score than an averageinventor.

A suitable method of rating inventors is for experts to review a groupof inventors' patents, and score each patent for some or all of theheadings of Table 4. Preferably, at least the Specialist, Architect, andInnovator's scores for the inventors are recorded, and a suitable meansof attributing contribution from the different inventors is applied. Onemethod is to apply the scores with diminishing weighting by the inventororder. For example, a patent may have a specialist score of 5, anarchitect score of 7, and an innovator score of 2, and the full scoresare applied to the first inventor, and contribution of half the scoresis applied to the second inventor, etc. This method typically requiresthe review of at least about 100 patents, more preferably at least 1000patents. Preferably, bibliographic, and processed bibliographic data isalso considered, including forward, backward, and self-citations,inventor order, number of co-inventors, and other data.

Alternatively, other publicly or privately held data such as financialdata, personnel data, HR data, tax, spending, travel, attendedconferences and internet data can be used for unsupervised machinelearning.

TABLE 4 Inventor Political Economic Serial Specialist ArchitectInnovator Network ID skill impact Architecting Innovator score scorescore score A B C D

-   -   Political skill is a ranking of the inventor's ability to        influence their organization. This can be for reasons of the        common good—e.g. Promoting the implementation of an invention,        or to promote themselves, or both.    -   Economic impact. This is a quantitative or semi quantitative        measure of the economic value generated by the inventor. An        approach for estimating the impact is to use a “but for”        approach, where it is hypothesized if the inventive team was in        place except for the inventor, estimating the difference in        likely economic impact. Economic impact may include sales, net        income, recognition and reputation, increased brand value, and        licensing.    -   Architecting. This assesses the inventor's ability to work with        a new group and to effectively contribute based on their        capabilities.    -   Serial Innovator. This assesses the role of chance (i.e., luck        vs. skill) with the inventor. In other words, this is an        assessment of how likely an inventor is to repeatedly make a        significant contribution. A high serial innovator ranking        suggests that the inventor has a high-level of skill at        innovation.    -   Specialist score. This is a score derived from a training set        for supervised machine learning, and broader data for        unsupervised data. The training set can be developed by a number        of means, including expert opinion, consensus, or both.    -   Architect score. This is a score derived from a training set for        supervised machine learning, and broader data for unsupervised        data. The training set can be developed by a number of means,        including expert opinion, consensus, or both.    -   Innovator score. This is a score derived from a training set for        supervised machine learning, and broader data for unsupervised        data. The training set can be developed by a number of means,        including expert opinion, consensus, or both. Specialist,        architect, and innovator scores can also incorporate data from        the scores of Table 3.    -   Network score. The network score may be developed by many means,        including expert opinion, consensus, or both. Network scores can        also incorporate data from the scores of Table 3.

FIG. 1 summarizes the possible steps and results for assessingindividual inventors. Many of the steps and results are optional,depending on the specific goals of the analysis.

Using the available data, a computer calculates inventor capabilities.These capabilities include technology domain access, exposure, andexpertise, co-inventor network, their inventive role, if they are aninventor-leader or enabler, and collaboration skills.

We have found that assessing inventor capabilities, and using thatassessment to estimate patent value forms a surprisingly consistent andaccurate predictor of inventor value creation that works across multipletypes of organizations. Using inventor capabilities as a predictor haslittle lag, and provides timely, quick, and accurate data in assessingthe value of recently published patents, and is relatively difficult tomanipulate. This approach can also be used to assess individualinventors for recruiting, compensation, engagement, and assignment.

The predictors for individual inventors may combined with that of otherinventors in simple or complex ways to determine and optimize the likelyfuture value generation of teams, organizations, and regions. Futurevalue generation may be optimized for groups of inventors based ontechnology and product development, phases, and goals.

As discussed previously, a computing device can be used in certainembodiments, to accomplish many of the methods discussed above and setforth in the examples below. A computing device will now be discussed inmore detail with respect to FIG. 38. It should be noted that this is oneexample of a computing device. There are many other possibilitiesincluding virtual devices which are formed in the cloud. So, a computingdevice may reside substantially all in one place. In other instances,memory which requires hardware may be cloud based. Software as a Servicemay also be used, which is software that is could based rather thanresident on a local service or computer. It should further be noted thathardware and software can form modules. These modules can be programmedto provide specific or general functions to carry out operations of themethods discussed herein. It should further be pointed out that thesemodules can be all software based or all hardware based. Generally, themodules are a combination of hardware and software. It should also benoted that software includes a set of steps executable by a computingdevice to cause one or more processors to do one or more steps of amethod. The software program can also be held on a medium such as acomputer memory or the like. Generally, when software instruction setsare downloaded from a source, the instruction set is held in mediabefore the transfer, during the transfer and after the transfer of asoftware instruction set to a machine. Generally, when a computingdevice is executing an instruction set it is a specialized machine.

FIG. 38 shows a diagrammatic representation of a computing device for amachine in the example electronic form of a computer system 4000, withinwhich a set of instructions for causing the machine to perform any oneor more of the methodologies discussed herein. The computing device canbe adapted to include the apparatus for determining various parametersand tools as described herein. In various example embodiments, themachine operates as a stand-alone device or can be connected (e.g.,networked) to other machines on a network. In a networked deployment,the machine can operate in the capacity of a server or a client machinein a server-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine can be apersonal computer (PC), a tablet PC, a set-top box (STB), a PersonalDigital Assistant (PDA), a smart phone, a cellular telephone, a portablemusic player (e.g., a portable hard drive audio device such as an MovingPicture Experts Group Audio Layer 3 (MP3) player, a web appliance, anetwork router, a switch, a bridge, or any machine capable of executinga set of instructions (sequential or otherwise) that specify actions tobe taken by that machine. Further, while only a single machine isillustrated, the term “machine” shall also be taken to include anycollection of machines that individually or jointly execute a set (ormultiple sets) of instructions to perform any one or more of themethodologies discussed herein.

The example computer system 4000 includes a processor or multipleprocessors 4002 (e.g., a central processing unit (CPU), a graphicsprocessing unit (GPU), arithmetic logic unit or all), and a main memory4004 a static memory 4006, which communicate with each other via a bus608. The computer system 4000 can further include a video display unit4010 (e.g., a liquid crystal displays (LCD) or a cathode ray tube (CRT)or the like). The computer system 4000 also includes an alphanumericinput device 4012 (e.g., a keyboard), a cursor control device 4014(e.g., a mouse), a disk drive unit 4016, a signal generation device 4018(e.g., a speaker) and a network interface device 4020.

The disk drive unit 4016 includes a computer-readable medium 4022 onwhich is stored one or more sets of instructions and data structures(e.g., instructions 4024) embodying or utilized by any one or more ofthe methodologies or functions described herein. The instructions 4024can also reside, completely or at least partially, within the mainmemory 4004 and/or within the processors 4002 during execution thereofby the computer system 4000. The main memory 4004 and the processors4002 also constitute, or can include, machine-readable media.

The instructions 4024 can further be transmitted or received over anetwork 4026 via the network interface device 4020 utilizing any one ofa number of well-known transfer protocols (e.g., Hyper Text TransferProtocol (HTTP), CAN, Serial, or Modbus).

While the computer-readable medium 4022 is shown in an exampleembodiment to be a single medium, the term “computer-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions and provide theinstructions in a computer readable form. The term “computer-readablemedium” shall also be taken to include any medium that is capable ofstoring, encoding, or carrying a set of instructions for execution bythe machine and that causes the machine to perform any one or more ofthe methodologies of the present application, or that is capable ofstoring, encoding, or carrying data structures utilized by or associatedwith such a set of instructions. The term “computer-readable medium”shall, accordingly, be taken to include, but not be limited to,solid-state memories, optical and magnetic media, tangible forms andsignals that can be read or sensed by a computer. Such media can alsoinclude, without limitation, hard disks, floppy disks, flash memorycards, digital video disks, random access memory (RAMs), read onlymemory (ROMs), and the like.

When the methods, discussed above, are programmed into a memory of ageneral purpose computer, such as the one described in FIG. 38, thecomputer and instructions form a special purpose machine. Theinstructions, when programmed into a memory of a general purposecomputer, are in the form of a non-transitory set of instructions. Theexample embodiments described herein can be implemented in an operatingenvironment comprising computer-executable instructions (e.g., software)installed on a computer, in hardware, or in a combination of softwareand hardware. Modules as used herein can be hardware including circuitryfor executing instructions. The computer-executable instructions can bewritten in a computer programming language or can be embodied infirmware logic. If written in a programming language conforming to arecognized standard, such instructions can be executed on a variety ofhardware platforms and for interfaces to a variety of operating systems.Although not limited thereto, computer software programs forimplementing the present method(s) can be written in any number ofsuitable programming languages such as, for example, Hypertext Mark upLanguage (HTML), Dynamic HTML, Extensible Mark up Language (XML),Extensible Stylesheet Language (XSL), Document Style Semantics andSpecification Language (DSSSL), Cascading Style Sheets (CSS),Synchronized Multimedia Integration Language (SMIL), Wireless Mark upLanguage (WML), Java™, Jini™, C, C++, Perl, UNIX Shell, Visual Basic orVisual Basic Script, Virtual Reality Mark up Language (VRML),ColdFusion™ or other compilers, assemblers, interpreters or othercomputer languages or platforms.

The present disclosure refers to instructions that are received at amemory system. Instructions can include an operational command, e.g.,read, write, erase, refresh, etc., an address at which an operationalcommand should be performed, and the data, if any, associated with acommand. The instructions can also include error correction data. Thefollowing examples can be accomplished with a computing device. Itshould be noted that the methods and examples discussed herein are notmerely methods that were previously implemented and now merelycomputerized. These methods are not only new and not obvious but presentan advancement in the area of computer arts and in the area ofpredicting values at patents and companies and of individuals tocorporations based on new and innovative treatment of data from assorteddatabases. These ideas are not abstract ideas merely run on a computerto make them more effective.

Example 1. Data Preparation

Patent data from the Fall, 2016 PATSTAT dataset, available from theEuropean Patent Office, was used to create a SQL database ofinternational patents and patent applications, along with bibliographicdata, citation data, and legal data. Company names and inventors werefurther disambiguated by comparing inventors to the associated number ofassignee organizations and countries.

The patent data with further disambiguated inventor data was processedto provide a list of inventors with data as exemplified in Table 2. Thelist of inventors with data was then processed to form the list ofinventors with characteristics and capabilities (ICC) as exemplified inTable 3. Finally, these results were combined with a training set ofinventors, and the machine learning estimated inventor characteristicsand capabilities (MLICC) was generated using Scikit-learn. The trainingset was created by expert opinion.

Example 2. Inventor Ranking Using Depth Level 3, Breadth Level 3, andNumber of Unique Co-Inventors

Inventors were ranked by the program using Depth level 3, Breadth level3, and the number of unique co-inventors. These metrics correlated withforward citations, but the r² was low at 0.23. By using all 5 IPC levelsto calculate Depth and Breadth, r² increased to 0.34. It has been foundthat using a plurality of levels associated with the InternationalPatent Classification levels is superior to using a single level of theInternational Patent Classification system for a technology or for apatent or grouping of patents. The r² value indicates the correlationbetween a model and it's fit to the actual results. As can be seen, theuse of multiple levels of IPC produces an increased value for r². Thisindicates a better fit between the model and the actual data.

Example 3. Organizational Trend Creating Patent Value Measured byForward Citations

228 organizations selected from a group from 300 organizationsgenerating the most US patents in 2013 were analysed using thebibliographic and legal data from the PATSTAT dataset. FIG. 2 shows thetrend in the average number of forward citations per patent (verticalaxis) for all 228 organizations from 1970 to 2013 (horizontal axis).

This example shows that forward citations are a lagging indicator due tothe time it takes for others to publish new art that references existingart. The chart 100 shows that forward citations 110 take about 7-10years to be about 90% of the long-term value.

Example 4. Organizational Trend Forecasting Patent Value ThroughInventor Characteristics

The process shown in FIG. 1 was used, where chart 200 shows forwardcitations 210 were calculated as described in Example 3, and forwardcitations were estimated by the process shown in FIG. 1, using theInventor Characteristics and Capabilities, as shown in Table 3, tocalculate the forward citation score for each inventor. The patentactivity for the inventors was used to predict the patents long-termaverage forward citations, shown in curve 220 of FIG. 3.

Example 5. Organizational Trend Forecasting Patent Value ThroughInventor Characteristics

The process shown in FIG. 1 was used to calculate the trend in inventoractivity as estimated by average Specialist, Architect, and Innovator(SAI) activity. Average activity of, for example, the Specialists wascalculated using the following equation:

$\begin{matrix}{{Sa} = {\sum\limits_{n = 1}^{N}\left( \frac{Ss_{n}*{InvPat}_{n}}{TotPat} \right)}} & {{Eq}.\mspace{14mu} 6}\end{matrix}$

Where Sa is the Specialist Activity, Ss_(n) is the specialist score forthe inventor n in the time period, InvPat_(n) is the number of patentspublished or patented for inventor n that have an earliest priority dateduring the time period, and TotPat is the total number of patents orpublications with the earliest priority date during the time period. Thesummation is for all N inventors in the 228 organizations described inExample 3.

Architect and Innovator activity was calculated in an analogous manneras for Specialists, where the Architect activity, Aa is calculated withthe following equation:

$\begin{matrix}{{Aa} = {\sum\limits_{n = 1}^{N}\left( \frac{As_{n}*{InvPat}_{n}}{TotPat} \right)}} & {{Eq}.\mspace{14mu} 7}\end{matrix}$

Where Aa is the Architect activity, As_(n) is the architect score forthe inventor n in the time period, InvPat_(n) is the number of patentspublished or patented for inventor n that have an earliest priority dateduring the time period, and TotPat is the total number of patents orpublications with the earliest priority date during the time period.

Innovator activity, Ia, is calculated with the following equation:

$\begin{matrix}{{Ia} = {\sum\limits_{n = 1}^{N}\left( \frac{{Is}_{n}*{InvPat}_{n}}{TotPat} \right)}} & {{Eq}.\mspace{14mu} 8}\end{matrix}$

Where Ia is the Innovator activity, Is_(n) is the Innovator score forthe inventor n in the time period, InvPat_(n) is the number of patentspublished or patented for inventor n that have an earliest priority dateduring the time period, and TotPat is the total number of patents orpublications with the earliest priority date during the time period.

The time period for equations 6-8 can be, for example, about 6 months, ayear, or longer.

The Specialist, Architect, and Innovator activities (SAI activities)were calculated as part of the Inventor Characteristics and Capabilitiescalculation, as shown in Table 4. The training set was based on aconsensus of experts based on opinions on a number of inventors.Additional data may be gathered from expert analysis of the inventor'spatents, publications, awards, and other data.

FIG. 4 shows the results in chart 300. Curve 350 is for Specialists,curve 330 is for Architects, and curve 340 is for Innovators. The scalefor the Specialist, Architects, and Innovators are individuallynormalized to improve the readability of the graph. In other words, onlythe relative changes in the curves should be considered as meaningful,not the absolute position. All three curves show a significant increasein the average over the 44-year time span. As with the forward citationscore, the SAI scores show much less time lag than for forwardcitations.

Example 6. Forecasting Organization Value

The trend for an individual organization was generated using theapproach described in Example 5, where only inventors that had assignedthe patent to the organization were considered. FIG. 6 shows the trendin the SAI activity for the organization. In FIG. 5, chart 400 showsnormalized SAI activity, vertical axis 410, for Specialists 450,Architects 430, and Innovators 440 from 1970 to 2014 on horizontal axis420. The impact of a change in leadership of the organization is shownat event 460, which correlated to a significant drop in Specialists,Architects, and Innovator activity. The drop in activity can be causedby a combination of inventors that have high scores for S, A, or Igenerating fewer patents, or for inventors who have low scoresincreasing their scores more slowly, or both.

Example 7. Forecasting Organization Value

The trend for an individual organization was generated using theapproach described in Example 5, where only inventors that had assignedthe patent to the organization were considered. FIG. 6 shows the trendin the SAI activity for the organization. Chart 500 shows normalized SAIactivity, vertical axis 510, for Specialists 550, Architects 530, andInnovators 540 from 1970 to 2014 on horizontal axis 520. The impact of achange in CEO leadership of the organization is shown at events 560 and570. Event 560 which correlated to a significant drop in Specialists andInnovator activity. Event 570 correlated to a significant increase inSpecialist activity.

Example 8. Comparing the Degree of Innovativeness of the Top Inventorsof Different Organizations

Three of the 228 organizations described in Example 3 were selected, andthe processed data described in Example 7 was used to identify the topinnovators and their Innovator scores. The inventors were sorted bytheir Innovator score and the ranked-ordered inventors by Innovatorscore was plotted as shown in FIG. 7. Chart 600 plots Innovator score610 by inventor number 620. Inventors for three organizations are shownin curves 630, 640, and 650. Organization 650 has a limited number ofhighly innovative inventors, organization 640 shows a much larger baseof highly innovative inventors, and organization 630 shows both a largebase of innovators with a smaller number of extremely innovativeinventors.

Example 9. Identifying High-Performing Specialists, Architects, andInnovators

Table 5 shows the Specialist's, Architect's, and Innovator's scores forone of the 228 organizations described in Example 3, where theorganization's inventors were originally sorted for those with the topinnovative scores, that is, Inventor 1 had the highest Innovator scorefor the organization and Inventor 2, the second highest, etc.

TABLE 5 Inventor Name Specialist Score Inventor 1 2.6 Inventor 2 1.2Inventor 24 0.8 Inventor 4 0.8 Inventor 4009 0.7 Inventor 4010 0.6Inventor 150 0.6 Inventor 37 0.6 Inventor 89 0.5 Inventor 4025 0.5Inventor Name Architect Score Inventor 1 6.7 Inventor 223 4.8 Inventor239 4.8 Inventor 247 4.8 Inventor 120 4.6 Inventor 222 4.5 Inventor 2254.5 Inventor 263 4.5 Inventor 287 4.5 Inventor 331 4.5 Inventor NameInnovator Score Inventor 1 2.4 Inventor 2 2.3 Inventor 3 2.0 Inventor 41.4 Inventor 5 0.9 Inventor 6 0.7 Inventor 7 0.7 Inventor 8 0.7 Inventor9 0.7 Inventor 10 0.7

Example 10. Identification of the Top Innovative Organizations by IPC

FIG. 8 shows chart 700, where the sum of Innovator scores 710 for anorganization's inventors are plotted against the sum of Architect scores720 for six organizations with patents in a specific IPC field.

The sum of innovator scores for each IPC field was calculated by thefollowing equation:

$\begin{matrix}{{SIa}_{IPC} = {{Normal}*{\sum\limits_{m = 1}^{M}\left( {\sum\limits_{n = 1}^{n}\left( \frac{{Is}_{n}}{\left( {{Inventor}\mspace{14mu}{Order}} \right)^{kd}} \right)} \right.}}} & {{Eq}.\mspace{14mu} 9} \\{{SAa}_{IPC} = {{Normal}*{\sum\limits_{m = 1}^{M}\left( {\sum\limits_{n = 1}^{n}\left( \frac{As_{n}}{\left( {{Inventor}\mspace{14mu}{Order}} \right)^{kd}} \right)} \right.}}} & {{Eq}.\mspace{14mu} 10}\end{matrix}$

Where kd is a value that typically ranges from 0.1 to 1, and is takenhere as being 1, Inventor order is the location of the inventor on thebibliographic data. The first listed inventor will have an inventororder of 1 (n=1), the second 2 (n=2), etc. to the Nth inventor. Is_(n)is the innovator score for inventor n on a patent. As_(n) is theinnovator score for inventor n on a patent. N is the number of inventorson a patent. m is the first patent in the group of M patents that have aspecified IPC. SIa_(IPC) is the sum of innovator scores, and SAa_(IPC)is the sum of architect scores. Normal is a scaling factor to normalizea graph values.

The values for Innovator's and Architect's scores are normalized. Eachorganization is represented by a bubble, 730 a, 730 b, 730 c, 730 d. 730e, and 730 f, where the area of the bubble is proportional to the numberof patent families having an earliest priority date from 2011 to 2016.

The results shown in FIG. 8 indicate a wide variation in inventiveskills between groups working in the same technology area. For example,the group(s) within a first company 730 a have substantially lower skillthan company 730 c in being able to invent by combing technologies asshown in the Architect scores 720. Furthermore, the group(s) withincompany 730 c have substantially lower skill in being able to work incomplex areas of technologies or markets or both than group(s) incompany 730 f.

Example 11. Comparing Organizations' Ability to Architect and Innovatefor all IPCs

FIGS. 9a and 9b compare two different organizations ability to architectand innovate in different areas of technology. FIG. 9a shows a plot 800that graphs the normalized sum of innovator scores 810 against thenormalized sum of architecting scores 820 for a first organization. Thedata bubbles 830 represent all of the level 3 IPCs for the organization,and the area of each bubble represents the total number of patentfamilies from 2011 to 2016 for that IPC. FIG. 9b shows a plot 900 thatgraphs the normalized sum of innovator scores 910 against the normalizedsum of architecting scores 920 for a second organization. The databubbles 930 represent all of the level 3 IPCs for the organization, andthe area of each bubble represents the total number of patent familiesfrom 2011 to 2016 for that IPC.

The charts indicate that both the first and second organizations havesimilar ability to architect inventions, but that the secondorganization has substantially less ability to innovate than the firstin many areas of technology.

Example 12. Comparing Several Organizations in Related BusinessesRelative Innovation Strength and Trend in Innovation

The level and trend of innovativeness of an organization can becalculated by comparing activity for one time period to another. This isshown in FIG. 10, where the innovation metric is calculated from the sumof Architect and Innovator scores for the organization's inventors whereeach Architect and Innovator score is proportionately weighted by thenumber of patents and sequence of the inventor. The total scores arecalculated for a first and a second time period. In this example, thefirst period is from 2010 to 2012, and the second period is from 2013 to2015.

FIG. 10 shows a chart 1000 which plots a change in innovation metric1010, where the change is the total score of the second period dividedby the first period. The innovation metric 1020 is the total score forthe second period.

This example compares private and public universities, showing, forexample, university 1030 b has a significant and increasing level ofactivity of innovative inventors, and that university 1030 a has arelatively low and dropping level of innovation.

Example 13. Comparing the Distribution and “T-Value” of the TopInventors for Several Organizations

The “T-value” (Tv) was calculated for all inventors using their backwardcitations.

It was assumed that inventor sequence impacted the weighting of depthand breadth, i.e., if an inventor was the first listed on the patent,they got full count of the impact of the IPCs. It was assumed that thesecond inventor had, on average, a lessor contribution, and so would gethalf credit, and third, third credit, and so on. This weighted count wasused to determine both breadth and depth.

Depth was calculated as the area under two curves, a first curve fordepth, and the second curve for breadth. The depth curve was calculatedby counting the IPCs at the first through fifth level cited for all ofthe inventor's patents. Each level was then sorted from the highest tothe lowest count, with each count being labeled with increasing wholenumbers starting with 1 (i).

The following equation was used to calculate depth:

X=i ^(A)−1

Y=count^(B)

Where count is the number of IPCs at number i. A was 0.25 and B was 0.5.The following equation was used to calculate breadth:

X=i ^(A)−1

Y=count^(B)

Where A was 1.7 and B was 0.25.

The areas under the first and second curves representing depth andbreadth were normalized and summed to create a “T-value” for eachinventor.

The inventors were associated with the assignees, and the inventors wereranked for five different organizations that were in the same industryand sector. Organizations were selected with similar number of annualpatent applications. Curve 1100 in FIG. 11 shows Tv 1110 plotted againstinventor number 1120, where curves 1130 a, b, c, d, and e are for thefive different organizations, and curves 1140 a, b, c, d, and e are thecorresponding fits to the curves. A power-law curve was used for thefit, using the equation:

Y=A*X ^(k)

Where the values for A and k are shown in Table 6.

This example shows that Tv follows a power-law distribution, and thatthe distribution is significantly different for the organizations.

Since the combination of depth and breadth is known to be important forinventors to solve problems, the curves indicate the relativeinnovativeness of different organizations.

TABLE 6 A k Organization 1140a 12.3 −0.49 Organization 1140b 7.07 −0.41Organization 1140c 4.57 −0.56 Organization 1140d 2.32 −0.48 Organization1140e 1.3 −0.5

Example 14. Forecasting Organic Growth of Organizations

FIG. 12 shows a means for forecasting future value of an organization,where chart 1100 shows a normalized innovation score 1110 against thedate 1120. Normalized average Architect scores 1130, Innovator scores1140, and Specialist scores 1150 are plotted against the organization'senterprise value 1210 and the organization's market cap 1220. There are4 changes in the chief executive officer (CEO) 1160, 1170, 1180, and1190, where CEOs 1160 and 1170 correlated with substantial declines inthe average scores for Specialists, Architects, and Innovators. Thereduced average capability of the inventors may be caused by attritionand reduced engagement. CEOs 1180 and 1190 correlated with astabilization of Architects, and a substantial increase in the averagescores of Innovators. The increase in the average activity of Innovatorsled an increase in both market capitalization value and enterprise valuefor the organization.

The impact of individual inventors to an organization can be determined,in other words, the inventor's value can be measured. Therefore, changesin organizational value can be assessed by tracking employment of one ormore individual inventors.

Example 15. Forecasting Inventor Contributions

Forward citations are a significant indicator to the degree that aninventor is advancing one or more fields. FIGS. 13A, 13B, 13C, and 13Dshow the forward citations of the patents of four inventors fromdifferent companies and universities. The inventors were chosen becausethey each generated many patents over two decades or more, they were themost prolific inventors in their organization, and they startedpatenting their inventions at a similar date (ca. 1990). The graphs inFIGS. 13A, 13B, 13C, and 13D, 1200, are for inventor 1210, 1220, 1230,and 1240. The vertical axes of the graphs are the number of forwardcitations, and the horizontal axes are the dates, in the month and yearformat (e.g. J-90 is Jan. 1, 1990).

The average and sum of forward citations are for patents with theearliest priority date within each date range. For example, the forwardcitations of patents with the earliest priority date between Jan. 1,1990 and Dec. 31, 1990 are summed and averaged for data available at aparticular date (here, using the Fall, 2016 Pat stat data), and the datais reflected on the curve. As a further example, inventor 1230's patentswith an earliest priority date in 1990 had an average of about 10forward citations in 1990, as measured with the Fall, 2016 Pat statdata.

For inventor 1210, the chart shows the sum of the number of forwardcitations for the inventor for each year in curve 1250, and the averagenumber of forward citations for each year in curve 1260.

For inventor 1220, the chart shows the sum of the number of forwardcitations for the inventor for each year in curve 1270, and the averagenumber of forward citations for each year in curve 1280.

For inventor 1230, the chart shows the sum of the number of forwardcitations for the inventor for each year in curve 1290, and the averagenumber of forward citations for each year in curve 1300.

For inventor 1240, the chart shows the sum of the number of forwardcitations for the inventor for each year in curve 1310, and the averagenumber of forward citations for each year in curve 1320.

All four inventors show a reduced number of forward citations in thelater years, likely in large part due to forward citations are a laggingindicator. Inventors 1210 and 1220 show distinct declines followed byincreases in their sum of forward citations. There is a smallerproportional change in the average number of forward citations. Thesudden drop then increase in the sum of forward citations may representepochs for the inventors when, for example, the inventor is changingfields of technology.

Each of the inventors retained significant impact as measured by the sumof forward citations for one or more decades. The sum of forwardcitations may be used to predict future impact. For example, knowing thesum of forward citations for patents filed in one five-year period canbe used to forecast the impact of the inventor's patents for futureperiods of time. The forecast in inventor contribution may be done forindividuals as well as for all or part of an organization's inventors.

Example 16. Forecasting Organizational Innovation Capacity

Example 15 shows that inventor contributions can be forecasted. Theinventive capacity of an organization can be estimated by developing aforecast for individual or groups of inventors, and using publicly orprivately held data to estimate the impact of changes in the combinedcontribution of individual inventors on the organization's innovationcapacity. The changes include the inventor leaving the organization dueto retirement or resignation, the inventor moving to a new organizationand adding to innovation capacity, or a change in the role of theinventor, such as moving to a management role or changing technologyfields.

By using sources of publicly or privately held data on the inventor,changes in the innovation capacity of an organization may be calculatedwith little or no lag, with, for example, daily updates.

Example 17. Forecasting the Innovation Capacity of New Organizations, orNew Groups within Organizations

The inventor data shown in FIGS. 13A, 13B, 13C, and 13D shows that atleast the sampling of prolific inventors rapidly develop a certain levelof innovation impact, and maintain this level of impact for many years.This means that the forward citation data, as well as other inventordata such as inventor depth, breadth, T-value, backward citation score,forward citation score, self-citation score, specialist score, architectscore, innovator score, and their network metrics, may be used toforecast their contributions when moving to a new team, group, ororganization. Furthermore, the efficacy of interaction of multipleinventors can be estimated by considering interactions of this data. Forexample, a team, group, or organization may be much more effective ifthere is a combination of areas of depth, and overlapping areas ofbreadth, than if the inventors are lacking areas of depth or breadthconsidered critical to what is needed to create high impact innovations.

Example 18. Forecasting the Stock Price of Companies with a High Portionof their Value Based on Intellectual Property

FIG. 14 shows the relationship 1400 between the ratio of AI 1410 to thegrowth in stock price 1420 of a group of companies, where the companiesare selected based on those where a substantial portion of the assets ofeach company is intellectual property. AI is calculated by the followingequation:

$\begin{matrix}{{AI} = \frac{\begin{matrix}\left( {{F_{I}{\Sigma_{m + 1}^{M}\left( {\Sigma_{n = 1}^{N}\left( \frac{{Is}_{n}}{\left( {{Inventor}\mspace{14mu}{Order}} \right)^{kn}} \right)} \right)}} +} \right. \\\left. {F_{A}{\Sigma_{m + 1}^{M}\left( {\Sigma_{n = 1}^{N}\left( \frac{{As}_{n}}{\left( {{Inventor}\mspace{14mu}{Order}} \right)^{kn}} \right)} \right)}} \right)\end{matrix}}{{time}\mspace{14mu}{period}}} & {{Eq}.\mspace{14mu} 11}\end{matrix}$

Where AI is an aggregated score indicating the level of innovationwithin an organization during the time period. The description of theterms is the same as for Equations 9 and 10. F_(I) is a scaling factorsfor innovator impact to an organization, and F_(A) is a scaling factorfor architect impact to an organization. Equation 11 may incorporateSpecialist scores in a similar manner, with Specialist contributionsbeing F_(S). This will create an SAI score for the organization. Thescores for F_(S), F_(A), and F_(I) may be determined by studies ofexisting organizations, or may be determined by using machine learningapproaches. A reasonable set of starting values for F_(S), F_(A), andF_(I)=1. The values of F_(S), F_(A), and F_(I) can be adjusted to createthe best fit between the aggregated score using Specialist, Architect,or Innovator scores, or combinations thereof (e.g AI and SAI scores) andorganizational performance metrics. Suitable organizational performancemetrics include stock price, operating income, operating margin, andmarket capitalization.

The analysis may include more complex response functions between changesin the innovation scores and changes in the financial metrics. Forexample, a change in organizational leadership may lead to a significantreduction in inventors with high SAI scores. That may initially reduceoperating and investment costs for the organization, possibly initiallyincreasing stock price. After a lag period though, the ability for theorganization to grow and respond to competition and opportunities can beimpaired, and stock price and other financial metrics can be adverselyaffected.

FIG. 14 charts 1400 the impact of a change in Architect-Innovator scores(AI) 1410 vs. the change in stock price 1420. Several companies in thesame sector and industry are plotted, for example company 1430, on chart1400. The stock price change was calculated as the ratio of the stockprice as of Nov. 20, 2017 to the stock price as of Nov. 20, 2016. Thechange in AI scores were calculated from the ratio of the average sum ofA and I scores from the time period from 2013 to 2016 to the average Aand I scores from 2010 to 2012. A linear fit 1440 to the company datahas a slope of 0.181 and an r² of 0.28.

FIG. 15 charts 1500 the impact of a change in Architect-Innovator scores(AI) 1510 vs. the change in stock price 1520. The same companies shownin FIG. 14 are shown, but for the time period between Nov. 20, 2013 andNov. 20, 2017. A linear fit 1440 to the company data has a slope of 1.75and an r² of 0.393.

FIG. 16 charts 1600 the slope and r² for linear fit vs. a starting dateto the companies shown in FIG. 14. The end of the time period is Nov.20, 2017.

FIGS. 15 and 16 use beginning dates for predicting stock price changethat precede the last date used to calculate the change in AI scores(2016). FIG. 12 shows that trends in changes in S, A, and I scores canbe seen in periods as short as 6 months from organizational changes, andthe effects on patent data can be seen about 18 months after that. Byadding employment data, predicting changes in AI and SAI can be evenfaster. For example, publicly available data can be accessed daily forchanges in employment of inventors that have a large contribution toSpecialist, Architect, and Innovator (SAI) scores, and those changes canbe used to calculate the impact on the organization.

The processes and systems shown below can be embodied within hardware,including one or more integrated circuits or an Application SpecificIntegrated Circuit (ASIC), or a combination thereof. The processor mayalso have GPUs. The order of the processing blocks should not beconsidered limiting. Rather, some of the processing blocks may executedin different configurations. Execution orders shown in FIG. 17 may beunidirectional or bidirectional between the inventor database, theapplication programming interface, and various queries.

FIG. 17 shows a suitable environment 1700 for implementing variousaspects of the claimed subject matter includes a computer 1701. Thecomputer 1701 includes a processing unit 1724, system memory 1726 whichmay include non-volatile memory 1730 and volatile memory 1728 and asystem bus 1702. The system bus 1702 connects system componentsincluding but not limited to the system memory 1726, the processing unit1724, a system bus 1702, and a codec 1708. The system bus connectssystem memory 1726 to the processing unit 1724. The system bus 1702 canbe any type of available bus including ISA, EISA, MSA, IDE, VESA, PCI,VLB, card bus, and USB, and other suitable bus architectures. The systembus may be combinations of different types of buses.

The disk storage 1706 may be magnetic storage, optical storage, orsolid-state storage, or the like. The computer 1701 can communicate toexternal network interface 1740 through communication connections 1710to connect to remote computers 1742 with associated memory storage 1744.The computer can also communicate to input devices 1736 and outputdevices 1738. The computer 1701 can be controlled by a combination of anoperating system 1716, modules 1718, data 1720, and application software1722.

FIG. 18 shows the commands between the inventor database, theapplication programming interface, and different queries. The queriesmay be unidirectional or bidirectional. Where bidirectional queries maybe used to modify the inventor database.

FIG. 18 is a diagram showing the commands between an inventor and atleast an application user interface, and different queries, according toan example embodiment.

FIG. 19 is a schematic of a computer system that includes memory and aprocessor, according to an example embodiment. The computing systemshown interacts and uses data from a number of public and privatedatabases including and internet data.

FIG. 20 is a schematic of a set of databases that are operated on by aprocessing system that executes an instruction set to reorganize,disambiguate and condition data received from one or more databases,according to an example embodiment.

FIG. 21 is a schematic of a set of databases that are operated on by aprocessing system that executes an instruction set to reorganize,disambiguate and condition data received from one or more databases, andfurther includes financial data and a machine learning training set andsubsystem, according to an example embodiment.

FIG. 22 is a schematic of a set of databases that are operated on by aprocessing system that executes an instruction set to reorganize,disambiguate and condition data received from one or more databases, andfurther includes financial data and a supervised machine learningtraining set and subsystem, according to an example embodiment.

FIG. 23 is a schematic of a set of databases that are operated on by aprocessing system that executes an instruction set to reorganize,disambiguate and condition data received from one or more databases, andfurther includes financial data and an unsupervised machine learningtraining set and subsystem, according to an example embodiment.

FIG. 24 is a flow diagram of a method of the invention, according to anexample embodiment. It should be noted that the methods set forth hereinare based on classification of invention information. It should bepointed out that similar methods can be utilized using other metricsassociated with inventors and inventing. The method includes identifyingone or more classification areas that are related to and area ofinterest 2410. The method 2400 also includes identifying an inventionwith high inventor performance amounts to a plurality of invention inclassification areas 2420. Reports are generated 2430 and the report orreports are sent to interested parties 2440.

FIG. 25 is a flow chart of a method 2500 for determining patent value,according to an example embodiment. The method includes create a sourceof patent data 2510. This can include public patent data and privatepatent data. Patent Offices around the world include patent data. Mostpatent offices classify the patents so related patents can be found. Inthe United States, for many years, had a patent classification systemand also kept a manual for classifying patents. Internationally, thereis also a classification system used by many countries. Each patentgenerally has a general classification and includes other classes aswell. R can be thought of as a primary classification and a secondaryclassification. Many indicate that these classification systems havelevels. This can be one source of patent data. It should be understoodthat others are also available. Once the source of patent data isdetermined, the data is analyzed to determine a patent value 2512.

FIG. 26 is a flow chart of a method 2600 for ranking and identifying orclassifying inventors, according to an example embodiment. The method2600 includes calculating a depth score for an inventor 2610, andcalculating a breadth score for the inventor 2612. Using these scores,the inventor is ranked relative to other inventors based on the scores2614. The inventor is also classified or identified based on at leastone of depth score and breadth score 2616, These scores can also be usedto calculate a forward citation prediction indicator for an inventor2618. It should be pointed out that an inventor can identify in aplurality of areas.

FIG. 27 is a flow chart of a method 2700 for comparing groups ofinventors, according to an example embodiment. The method includescalculating a depth score for a plurality of inventors 2710, andcalculating a breadth score for the plurality of inventors 2712. Thegroups are classified or identified within the plurality of inventorsbased on at least one of depth score and breadth score of the groups2714. It should be noted that in one embodiment, inventors can beclassified or identified in a single group. In other embodiments, aninventor can be identified as belonging to more than one group. Themethod 2700 also includes comparing groups of the inventors other groupsof inventors based on the scores 2716.

FIG. 28 is a flow chart of a method 2800 for predicting forward citationof a patent document, according to an example embodiment. The method2600 includes determining a forward citation score for a first inventorof a patent document based on citations to other patents associated withthe first inventor 2810, and determining a forward citation score for asecond inventor of the patent document based on citations to otherpatents associated with the second inventor 2812. The method 2800 alsoincludes forecasting the forward citation of the patent based on thedetermined forward citation score for the first inventor and thedetermined forward citation score for the second inventor 2814. A patentvalue is forecast in response to the predicted number for the forwardcitation score of the patent document 2816.

FIG. 29 is a flow chart of a method 2900 for predicting forward citationof a patent document, according to an example embodiment. The method2900 includes classifying or identifying a first inventor as aspecialist having a high depth and low breadth on a number of patents inwhich the first inventor has previously been named as an inventor 2910.The method 2900 also includes determining the activity level of thefirst inventor 2912. A second inventor is classified or identified as aspecialist having a high depth and low breadth on a number of patents inwhich the second inventor has previously been named as an inventor 2914.The activity level of the second inventor is also determined 2916. Theactivity level of the first inventor and the activity level of thesecond inventor are averaged 2918. The method also includes predictingthe value of the patent based on the average activity number 2920.

FIG. 30 is a flow chart of a method 3000 for predicting an upswing or adecline in growth of the organization, according to an exampleembodiment. The method 3000 includes identifying a set of inventorsassociated with an assignee company 3010. The inventors in the set areclassified or identified as specialists, architects, and innovatorsbased on classifications of the patents or patent publications on whichan individual inventor is named, a specialist having associated with theinvention a particular inventor having a depth above a threshold and abreadth below a breadth threshold, an architect having a depth above thedepth threshold and a breadth above a breadth threshold, and aninnovator having high depth and high breadth and with coinventors thatchange above a selected frequency 3012. An event is identified 3014. Thelevel of change in activity levels of the specialists in the set, theinnovators in the set, and the architects in the set is monitored todetermine the effect of the event 3016. In some embodiments, thearchitect scores, specialist scores, and innovator scores are normalizedover time 3018. The normalized scores are plotted against anorganizations enterprise value and market cap 3020. Up and down trendsare noted after certain events. The up and down trends are used toforecast an upswing or a decline in growth of the organization 3022.

FIG. 31 is a flow chart of a method 3100 for predicting an upswing or adecline in growth of the organization, according to an exampleembodiment. The method 3100 includes selecting criteria for identifyinga first set of patent documents from a first organization andidentifying a second set of patent documents from a second organization3110. Method 3100 also includes identifying first organizationinnovators in the first organization based on patent classifications ofpatents within the set that the inventors are named on, the innovatorsidentified by using classification data associated with the inventionsand reviewing characteristics of coinventors named on patent documentsthey were named on 3112. Method 3100 also includes identifying secondorganization innovators in the second organization based on patentclassifications of patents within the set that the inventors are namedon 3114. The number of innovators in the first organization is comparedwith the number of innovators in the second organization 3116. This caninclude comparing the scores or average scores of innovation of the twoorganizations. It should be noted that inventors in these groups can beclassified or identified as belonging to more than one group. In someembodiments, individual inventors may be constrained to a single group.It should be noted that this can be done on a specific technology areaso that innovation in one technology area can be compared betweenorganizations.

FIG. 32 is a computer system 3200 for predicting forward citations foran inventor, according to an example embodiment. The computer systemincludes a processor 3210, and a memory 3212 attached or communicativelycoupled to a bus 3214. A number of modules are also attached to the bus3214. The modules can be a hardware, software, or a combination ofsoftware and hardware. They can be stand alone or can share memory 3212or another source of memory. They can have stand alone processors or canshare processing with the processor 3210. The modules include a depthscore calculation module for calculating a depth score for an inventor3216, a breadth score calculation module for calculating a breadth scorefor the inventor 3218, a ranking module for ranking the inventorrelative to other inventors based on the scores 3220, and a forwardcitation prediction indicator calculator for calculating a forwardcitation prediction indicator for an inventor 3222.

FIG. 33 is a computer system 3300 for predicting forward citations for apatent, according to an example embodiment. The computer system 3300includes a processor 3310, and a memory 3312 attached or communicativelycoupled to a bus 3314. A number of modules are also attached to the bus3314. The modules can be a hardware, software, or a combination ofsoftware and hardware. They can be stand alone or can share memory 3312or another source of memory. They can have stand alone processors or canshare processing with the processor 3310. The modules include a forwardcitation score module for determining a forward citation score for afirst inventor of a patent document based on citations to other patentsassociated with the first inventor and for determining a forwardcitation score for a second inventor of the patent document based oncitations to other patents associated with the second inventor 3316.Another module is a forecasting module for forecasting the forwardcitation of the patent based on the determined forward citation scorefor the first inventor and the determined forward citation score for thesecond inventor 3318.

FIG. 34 is a computer system 3400 for predicting the value of the patentbased on the activity of a plurality of inventors, according to anexample embodiment. The computer system 3400 includes a processor 3410,and a memory 3412 attached or communicatively coupled to a bus 3414. Anumber of modules are also attached to the bus 3414. The modules can bea hardware, software, or a combination of software and hardware. Theycan be stand alone or can share memory 3412 or another source of memory.They can have stand alone processors or can share processing with theprocessor 3410. The modules include a classification module forclassifying a first inventor as a specialist having a high depth and lowbreadth on a number of patents in which the first inventor haspreviously been named as an inventor; and classifying a second inventoras a specialist having a high depth and low breadth on a number ofpatents in which the second inventor has previously been named as aninventor 3416. Another module attached to the bus 3414 is an activitylevel determination module for determining the activity level of thefirst inventor; and determining the activity level of the secondinventor; and averaging the activity level of the first inventor and theactivity level of the second inventor 3418. A prediction module forpredicting the value of the patent based on the average activity number3420 is also attached to the bus. Again, it should be noted that theinventors can be identified in one of several classes. In otherembodiments, the inventors can be identified as belonging to severalclasses of inventors.

FIG. 35 is a computer system 3500 for predicting the value of the patentbased on the activity of a plurality of inventors, according to anexample embodiment. The computer system 3500 includes a processor 3510,and a memory 3512 attached or communicatively coupled to a bus 3514. Anumber of modules are also attached to the bus 3514. The modules can bea hardware, software, or a combination of software and hardware. Theycan be stand alone or can share memory 3512 or another source of memory.They can have stand alone processors or can share processing with theprocessor 3510. The modules include an identification module foridentifying a set of inventors associated with an assignee company andidentifying events related to sets of inventors 3516, a classificationmodule for classifying the inventors in the set into at leastspecialists, architects, and innovators based on classifications of thepatents or patent publications on which an individual inventor is named,a specialist having associated with the invention a particular inventorhaving a depth above a threshold and a breadth below a breadththreshold, an architect having a depth above the depth threshold and abreadth above a breadth threshold, and an innovator having high depthand high breadth and with coinventors that change above a selectedfrequency 3518, and a monitor for monitoring the level of change inactivity levels of the specialists in the set, the innovators in theset, and the architects in the set to determine the effect of the event3520. Also attached to the bus is a value module for monitoring thevalue of an organization when the event is an event associated with theorganization and determining trends in value 3522.

FIG. 36 is a computer system 3600 for comparing the amount of innovationfor two or more organizations, according to an example embodiment. Thecomputer system 3600 includes a processor 3610, and a memory 3612attached or communicatively coupled to a bus 3614. A number of modulesare also attached to the bus 3614. The modules can be a hardware,software, or a combination of software and hardware. They can be standalone or can share memory 3612 or another source of memory. They canhave stand alone processors or can share processing with the processor3610. The modules include a selection module for selecting criteria foridentifying a first set of patent documents from a first organizationand identifying a second set of patent documents from a secondorganization 3616, and a identication module for identifying firstorganization innovators in the first organization based on patentclassifications of patents within the set that the inventors are namedon, the innovators identified by using classification data associatedwith the inventions and reviewing characteristics of coinventors namedon patent documents they were named on; and identifying secondorganization innovators in the second organization based on patentclassifications of patents within the set that the inventors are namedon 3618. The computer system 3600 also includes a comparator forcomparing the number of innovators in the first organization with thenumber of innovators in the second organization 3620. Several othermodules are also attached to the bus 3614 in the event the databasechanges over the time frame. These include a search module for searchingdatabases for changes in status of at least one of the innovators in thegroup of innovators in one of a first organization or a secondorganization 3622, and a scoring module for recalculating an innovationscore for the innovators hi the at least one organization 3624.

FIG. 37 is a flow chart of a method 3700 for identifying inventors orothers in an organization, according to an example embodiment. Themethod 3100 includes determining a first set of inventions invented by afirst set of inventors 3710, determining a second set of inventionsinvented by a second set of inventors 3712, and determining a third setof inventions invented by a third set of inventors 3714. The method 3700further includes calculating a depth score for a first set of inventorsusing a publically available patent classification scheme forclassifying patents 3716, calculating a breadth score for the first setof inventors using a publically available patent classification schemefor classifying patents 3718, calculating a depth score for a second setof inventors using a publically available patent classification schemefor classifying patents 3720. And calculating a breadth score for thesecond set of inventors using a publically available patentclassification scheme for classifying patents 3722. The method 3700 alsoincludes characterizing one of the first set, second set, or third setof inventors as innovators 3724, characterizing one of the first set,second set, or third set of inventors as specialists 3726, andcharacterizing one of the first set, second set, or third set ofinventors as architects 3728. Given these criteria, various predictionsand parameters can be determined. Comparisons can be made betweenorganizations within companies, or between separate companies.

FIG. 38 shows a diagrammatic representation of a computing device for amachine in the example electronic form of a computer system 4000, withinwhich a set of instructions for causing the machine to perform any oneor more of the error correction methodologies discussed herein can beexecuted or is adapted to include the apparatus for error correction asdescribed herein. In various example embodiments, the machine operatesas a standalone device or can be connected (e.g., networked) to othermachines. In a networked deployment, the machine can operate in thecapacity of a server or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine can be a personal computer (PC), atablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), acellular telephone, a portable music player (e.g., a portable hard driveaudio device such as a Moving Picture Experts Group Audio Layer 3 (MP3)player, a web appliance, a network router, a switch, a bridge, or anymachine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein.

The example computer system 4000 includes a processor or multipleprocessors 4002 (e.g., a central processing unit (CPU), a graphicsprocessing unit (GPU), arithmetic logic unit or all), and a main memory4004 and a static memory 4006, which communicate with each other via abus 4008. The computer system 4000 can further include a video displayunit 4010 (e.g., a liquid crystal display (LCD) or a cathode ray tube(CRT)). The computer system 4000 also includes an alphanumeric inputdevice 4012 (e.g., a keyboard), a cursor control device 4014 (e.g., amouse), a disk drive unit 4016, a signal generation device 4018 (e.g., aspeaker) and a network interface device 4020.

The disk drive unit 4016 includes a computer-readable medium 4022 onwhich is stored one or more sets of instructions and data structures(e.g., instructions 4024) embodying or utilized by any one or more ofthe methodologies or functions described herein. The instructions 4024can also reside, completely or at least partially, within the mainmemory 4004 and/or within the processors 4002 during execution thereofby the computer system 4000. The main memory 4004 and the processors4002 also constitute machine-readable media.

The instructions 4024 can further be transmitted or received over anetwork 4026 via the network interface device 4020 utilizing any one ofa number of well-known transfer protocols (e.g., Hyper Text TransferProtocol (HTTP), CAN, Serial, or Modbus).

While the computer-readable medium 4022 is shown in an exampleembodiment to be a single medium, the term “computer-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store the one or more sets of instructions and provide theinstructions in a computer readable form. The term “computer-readablemedium” shall also be taken to include any medium that is capable ofstoring, encoding, or carrying a set of instructions for execution bythe machine and that causes the machine to perform any one or more ofthe methodologies of the present application, or that is capable ofstoring, encoding, or carrying data structures utilized by or associatedwith such a set of instructions. The term “computer-readable medium”shall accordingly be taken to include, but not be limited to,solid-state memories, optical and magnetic media, tangible forms andsignals that can be read or sensed by a computer. Such media can alsoinclude, without limitation, hard disks, floppy disks, flash memorycards, digital video disks, random access memory (RAMs), read onlymemory (ROMs), and the like.

The specification details many inventions. Some of the inventions arefurther detailed in the following paragraphs. The following listing ofinventions is not exhaustive. It should be noted that there are manyother possible inventions described herein.

A method for determining intellectual property value, comprising:creating a source of patent data; and analyzing the data to determine apatent value.

An instruction set executable on a machine that includes a processor anda memory, the instruction set executable on the machine to cause to themachine to: create a source of patent data; and analyze the data todetermine a patent value.

A media carrying an instruction set executable on a machine, the machinefurther comprising: a processor; and a memory associated with theprocessor. The instruction set is executable on the processor to causeto the machine to: create a source of patent data; and analyze the datato determine a patent value.

A method comprising: calculating a depth score for an Inventor;calculating a breadth score for the inventor; and ranking the inventorrelative to other inventors based on the scores. In one embodiment aninventor's capability for generating high-value patents is ranked basedon at least one of the depth score or the breadth scores. The scores arebased on a plurality of patent classifications associated with patentsobtained by the inventor. The method also further includes calculating acollaboration score reflective of the inventors ability to work withother inventors. The plurality of patent classifications have a firstlevel and a second level. At least one of the depth score or the breadthscore are calculated from at least the first level patent classificationand the second level patent classification. The method furthercomprising calculating a forward citation prediction indicator for aninventor. The method further comprising evaluating capability in one ormore inventive categories based at least on the depth and breadth scoresfor the inventor. The capability is determined for an inventor or for aplurality of inventors. The method can also include disambiguatingpatent data which further comprises disassociating certain patents froma list of patents erroneously attributed to an inventor. The method alsoincludes identifying an inventor as a specialist when the inventor has adepth score above a threshold depth score and a breath value which isbelow a breadth threshold score, identifying an inventor as an innovatorwhen the inventor has a depth score above a threshold depth score and abreadth value above a breadth threshold score, or identifying aninventor as an architect when the inventor has a depth score above athreshold depth score and a breadth value above a breadth thresholdscore, and the inventor has co-inventors with different specialty areas.It should be noted that one inventor can be identified as one or more ofan architect, innovator or specialist. The method can also include:reviewing one or more co-inventors on a collection of patent documentson which the inventor is listed: and ranking the inventor based on thenumber of unique one or more co-inventors associated with the inventoron the collection of patent documents. The method can includeidentifying an inventor as an innovator when the inventor has a depthscore above a threshold depth score and a breadth value above a breadththreshold score, and with co-inventors that change above a selectedfrequency.

A method for forecasting patent value comprising: determining a forwardcitation score for a first inventor of a patent document based oncitations to other patents associated with the first inventor; anddetermining a forward citation score for a second inventor of the patentdocument based on citations to other patents associated with the secondinventor; and forecasting the forward citation of the patent based onthe determined forward citation score for the first inventor and thedetermined forward citation score for the second inventor. The methodfurther comprising averaging the forward citation score for the firstinventor and the forward citation score for the second inventor toarrive at a number to predict the forward citation score of the patentdocument. The method for forecasting patent value further comprisingvaluing the patent document in response to the predicted number for theforward citation score of the patent document.

A method for predicting a value of a patent that includes a plurality ofinventors comprising: identifying a first inventor as a specialisthaving a high depth and low breadth on a number of patents in which thefirst inventor has previously been named as an inventor; determining theactivity level of the first inventor; identifying a second inventor as aspecialist having a high depth and low breadth on a number of patents inwhich the second inventor has previously been named as an inventor;determining the activity level of the second inventor; and averaging theactivity level of the first inventor and the activity level of thesecond inventor; and predicting the value of the patent based on theaverage activity number. The above method further comprising:identifying a third inventor as an architect having a high depth andhigh breadth on a number of patents in which the third inventor haspreviously been named as an inventor; determining the activity level ofthe third inventor; identifying a fourth inventor as an architect havinga high depth and high breadth on a number of patents in which the fourthinventor has previously been named as an inventor; determining theactivity level of the fourth inventor; and averaging the activity levelof the third inventor and the activity level of the fourth inventor; andpredicting the value of the patent based on the average activity numberfor specialists and the average activity number for architects. Theabove method further comprising: identifying a fifth inventor as aninnovator having a high depth and high breadth on a number of patents inwhich the fifth inventor has previously been named as an inventor;determining the activity level of the fifth inventor; identifying asixth inventor as an inovator having a high depth and high breadth on anumber of patents in which the sixth inventor has previously been namedas an inventor; determining the activity level of the sixth inventor;and averaging the activity level of the fifth inventor and the activitylevel of the sixth inventor; and predicting the value of the patentbased on the average activity number for specialists and the averageactivity number for innovators.

A method of monitoring the value of an organization comprising:identifying a set of inventors associated with an assignee company;identifying the inventors in the set as specialists, architects, andinnovators based on classifications of the patents or patentpublications on which an individual inventor is named, a specialisthaving associated with the invention a particular inventor having adepth above a threshold and a breadth below a breadth threshold, anarchitect having a depth above the depth threshold and a breadth above abreadth threshold, and an innovator having high depth and high breadthand with co-inventors that change above a selected frequency; andIdentifying an event; and monitoring the level of change in activitylevels of the specialists in the set, the innovators in the set, and thearchitects in the set to determine the effect of the event. The eventcan be an event associated with the organization. Other events outsidethe organization can also be monitored. The above method furthercomprising determining a trend in activity levels after the event, andassociating an increased value with the organization in response to anupward trend in increased levels of activity of the specialists in theset, the innovators in the set, and the architects in the set. The abovemethod further comprising: normalizing architect scores, specialistscores, and innovator scores over time; and plotting the normalizedscores against an organizations enterprise value and market cap; andnoting up and down trends after certain events, and using the up anddown trends to forecast an upswing or a decline in growth of theorganization. The above method further comprising: associating aninnovation score with the innovators; associating an architect scorewith the architects; and associating an specialist score with thespecialists in an organization; and ranking the innovators, architectsand specialists in an organization based on the respective scores forthe individuals in these categories.

A method of comparing the relative innovation between organizationscomprising: selecting criteria for identifying a first set of patentdocuments from a first organization and identifying a second set ofpatent documents from a second organization; and identifying firstorganization innovators in the first organization based on patentclassifications of patents within the set that the inventors are namedon, the innovators identified by using classification data associatedwith the inventions and reviewing characteristics of co-inventors namedon patent documents they were named on; identifying second organizationinnovators in the second organization based on patent classifications ofpatents within the set that the inventors are named on; and comparingthe number of innovators hi the first organization with the number ofinnovators in the second organization. The above method wherein thenumbers of innovators is normalized. The above method wherein selectingthe criteria for identifying a first set of patent documents from thefirst organization and for identifying a second set of patent documentsfrom the second organization includes a first time period and a secondtime period. The above method wherein selecting the criteria foridentifying a first set of patent documents from the first organizationand for identifying a second set of patent documents from the secondorganization includes a specific technology field as depicted by apatent classification. The above method further comprising: searchingdatabases for changes in status of at least one of the innovators in thegroup of innovators in one of a first organization or a secondorganization; and recalculating an innovation score for the innovatorsin the at least one organization. The above method further comprising:Identifying first organization architects in the first organizationbased on patent classifications of patents within the set that theinventors are named on; the architects identified by usingclassification data associated with the inventions and reviewingcharacteristics of co-inventors named on patent documents they werenamed on; and Identifying second organization innovators in the secondorganization based on patent classifications of patents within the setthat the inventors are named on. The above method wherein the selectioncriteria for the subset of patents includes a plurality of patentclassifications.

An intellectual property valuing system comprising: a processor; amemory; and an instruction set stored in memory, the instruction setexecutable on the processor to cause to the processor to: calculate adepth score for an inventor; calculate a breadth score for the inventor;and rank the inventor relative to other inventors based on the scores.The above intellectual property valuing system wherein the instructionset further causes the processor to rank the inventor in terms ofcapability for generating high-value patents is based on at least one ofthe depth score or the breadth scores. The above intellectual propertyvaluing system wherein the scores are based on a plurality of patentclassifications associated with patents obtained by the inventor. Theabove intellectual property valuing system wherein the instruction setfurther causes the processor to calculate a collaboration scorereflective of the inventors ability to work with other inventors. Theabove intellectual property valuing system wherein the plurality ofclassifications have a first level and a second level, wherein theinstruction set further causes the processor to calculate at least oneof the depth score or the breadth score from at least the first levelclassification and the second level classification. The aboveintellectual property valuing system wherein the instruction set furthercauses the processor to calculate a forward citation predictionindicator for an inventor. The above intellectual property valuingsystem wherein the instruction set further causes the processor toevaluate a capability in one or more inventive categories based at leaston the depth and breadth scores for the inventor. The above intellectualproperty valuing system wherein the capability is determined for aninventor. The above intellectual property valuing system wherein thecapability is determined for a plurality of inventors. The aboveintellectual property valuing system wherein the instruction set furthercauses the processor to disambiguate patent data which further comprisesdisassociating certain patents from a list of patents erroneouslyattributed to an inventor. The above intellectual property valuingsystem wherein the instruction set further causes the processor toidentify an inventor as a specialist when the inventor has a depth scoreabove a threshold depth score and a breath value which is below abreadth threshold score. The above intellectual property valuing systemwherein the instruction set further causes the processor to identify aninventor as an innovator when the inventor has a depth score above athreshold depth score and a breadth value above a breadth thresholdscore. The intellectual property valuing system wherein the instructionset further causes the processor to identify an inventor as an architectwhen the inventor has a depth score above a threshold depth score and abreadth value above a breadth threshold score, and the inventor hasco-inventors with different specialty areas. The above intellectualproperty valuing system wherein the instruction set further causes theprocessor to: review one or more co-inventors on a collection of patentdocuments on which the inventor is listed: and rank the inventor basedon the number of unique one or more co-inventors associated with theinventor on the collection of patent documents. The above intellectualproperty valuing system wherein the instruction set further causes theprocessor to identify an inventor as an innovator when the inventor hasa depth score above a threshold depth score and a breadth value above abreadth threshold score, and with co-inventors that change above aselected frequency.

An intellectual property valuing system comprising: a processor; amemory; and an instruction set stored in memory. The instruction set isexecutable on the processor to cause to the the processor to forecastpatent value comprising: determining a forward citation score for afirst inventor of a patent document based on citations to other patentsassociated with the first inventor; and determining a forward citationscore for a second inventor of the patent document based on citations toother patents associated with the second inventor; and forecasting theforward citation of the patent based on the determined forward citationscore for the first inventor and the determined forward citation scorefor the second inventor. The above intellectual property valuing systemwherein the instruction set further causes the processor to average theforward citation score for the first inventor and the forward citationscore for the second inventor to arrive at a number to predict theforward citation score of the patent document. The above intellectualproperty valuing system wherein the instruction set further causes theprocessor to value the patent document in response to the predictednumber for the forward citation score of the patent document.

An intellectual property valuing system comprising: a processor; amemory; and an instruction set stored in memory. The instruction set isexecutable on the processor to cause to the processor to: predict avalue of a patent that includes a plurality of inventors by: identifyinga first inventor as a specialist having a high depth and low breadth ona number of patents in which the first inventor has previously beennamed as an inventor; determining the activity level of the firstinventor; identifying a second inventor as a specialist having a highdepth and low breadth on a number of patents in which the secondinventor has previously been named as an inventor; determining theactivity level of the second inventor; and averaging the activity levelof the first inventor and the activity level of the second inventor; andpredicting the value of the patent based on the average activity number.The above intellectual property valuing system wherein the instructionset further causes the processor to: identify a third inventor as anarchitect having a high depth and high breadth on a number of patents inwhich the third inventor has previously been named as an inventor;determine the activity level of the third inventor; identify a fourthinventor as an architect having a high depth and high breadth on anumber of patents hi which the fourth inventor has previously been namedas an inventor; determine the activity level of the fourth inventor; andaverage the activity level of the third inventor and the activity levelof the fourth inventor; and predict the value of the patent based on theaverage activity number for specialists and the average activity numberfor architects. The above intellectual property valuing system whereinthe instruction set further causes the processor to: identify a fifthinventor as an innovator having a high depth and high breadth on anumber of patents in which the fifth inventor has previously been namedas an inventor; determine the activity level of the fifth inventor;identify a sixth inventor as an innovator having a high depth and highbreadth on a number of patents in which the sixth inventor haspreviously been named as an inventor; determine the activity level ofthe sixth inventor; and average the activity level of the fifth Inventorand the activity level of the sixth inventor; and predict the value ofthe patent based on the average activity number for specialists and theaverage activity number for innovators.

An intellectual property valuing system comprising: a processor; amemory; and an instruction set stored in memory. The instruction setexecutable on the processor to cause to the processor to monitor thevalue of an organization by: identifying a set of inventors associatedwith an assignee company; identifying the inventors in the set into atleast specialists, architects, and innovators based on classificationsof the patents or patent publications on which an individual inventor isnamed, a specialist having associated with the invention a particularinventor having a depth above a threshold and a breadth below a breadththreshold, an architect having a depth above the depth threshold and abreadth above a breadth threshold, and an innovator having high depthand high breadth and with co-inventors that change above a selectedfrequency; and identifying an event; monitoring the level of change inactivity levels of the specialists in the set, the innovators in theset, and the architects in the set to determine the effect of the event.The above intellectual property valuing system wherein the instructionset further causes the processor to monitor the value of an organizationwhen the event is an event associated with the organization. The aboveintellectual property valuing system wherein the instruction set furthercauses the processor to determine a trend in activity levels after theevent, and associating an increased value with the organization inresponse to an upward trend in increased levels of activity of thespecialists in the set, the innovators in the set, and the architects inthe set. The above intellectual property valuing system wherein theinstruction set further causes the processor to: normalize architectscores, specialist scores, and innovator scores over time; and plot thenormalized scores against an organizations enterprise value and marketcap; and note up and down trends after certain events, and use the upand down trends to forecast an upswing or a decline in growth of theorganization. The above intellectual property valuing system wherein theinstruction set further causes the processor to: associate an innovationscore with the innovators; associate an architect score with thearchitects; and associate an specialist score with the specialists in anorganization; and rank the innovators, architects and specialists in anorganization based on the respective scores for the individuals in thesecategories.

An intellectual property valuing system comprising: a processor; amemory; and an instruction set stored in memory. The instruction setexecutable on the processor to cause to the processor to compare therelative innovation between organizations by: selecting criteria foridentifying a first set of patent documents from a first organizationand identifying a second set of patent documents from a secondorganization; identifying first organization innovators in the firstorganization based on patent classifications of patents within the setthat the inventors are named on, the innovators identified by usingclassification data associated with the inventions and reviewingcharacteristics of co-inventors named on patent documents they werenamed on; identifying second organization innovators in the secondorganization based on patent classifications of patents within the setthat the inventors are named on; and comparing the number of innovatorsin the first organization with the number of innovators in the secondorganization. The above intellectual property valuing system wherein theinstruction set further causes the processor to normalize the numbers ofinnovators. The above intellectual property valuing system wherein theinstruction set further causes the processor to select the criteria foridentifying a first set of patent documents from the first organizationand to identify a second set of patent documents from the secondorganization includes a first time period and a second time period. Theabove intellectual property valuing system wherein the instruction setfurther causes the processor to select the criteria for identifying afirst set of patent documents from the first organization and foridentifying a second set of patent documents from the secondorganization includes a specific field as depicted by a patentclassification. The above intellectual property valuing system ofwherein the instruction set further causes the processor to: searchdatabases for changes in status of at least one of the innovators in thegroup of innovators in one of a first organization or a secondorganization; and recalculate an innovation score for the innovators inthe at least one organization. The above intellectual property valuingsystem wherein the instruction set further causes the processor to:Identify a first organization architects in the first organization basedon patent classifications of patents within the set that the inventorsare named on, the architects identified by using classification dataassociated with the inventions and reviewing characteristics ofco-inventors named on patent documents they were named on; and Identifya second organization innovators in the second organization based onpatent classifications of patents within the set that the inventors arenamed on. The above intellectual property valuing system wherein theinstruction set further causes the processor to select criteria for thesubset of patents that includes a plurality of patent classifications.

A non-transitory machine-readable medium providing instructions that,when executed by a machine, cause the machine to perform operationscomprising: calculate a depth score for an inventor; calculate a breadthscore for the inventor; and rank the inventor relative to otherinventors based on the scores. The above non-transitory machine-readablemedium providing instructions that further causes the machine to rankthe inventor in terms of capability for generating high-value patents isbased on at least one of the depth score or the breadth scores. Theabove non-transitory machine-readable medium wherein the scores arebased on a plurality of patent classifications associated with patentsobtained by the inventor. The above non-transitory machine-readablemedium providing instructions that further causes the machine tocalculate a collaboration score reflective of the inventors ability towork with other inventors. The above non-transitory machine-readablemedium wherein the plurality of classifications have a first level and asecond level, wherein the non-transitory machine-readable mediumproviding instructions that further causes the machine to calculate atleast one of the depth score or the breadth score from at least thefirst level classification and the second level classification. Theabove non-transitory machine-readable medium providing instructions thatfurther causes the machine to calculate a forward citation predictionindicator for an inventor. The above non-transitory machine-readablemedium providing instructions that further causes the machine toevaluate a capability in one or more inventive categories based at leaston the depth and breadth scores for the inventor. The abovenon-transitory machine-readable medium wherein the capability isdetermined for an inventor. The above non-transitory machine-readablemedium wherein the capability is determined for a plurality ofinventors. The above non-transitory machine-readable medium providinginstructions that further causes the machine to disambiguate patent datawhich further comprises disassociating certain patents from a list ofpatents erroneously attributed to an inventor. The above non-transitorymachine-readable medium providing instructions that further causes themachine r to identify an inventor as a specialist when the inventor hasa depth score above a threshold depth score and a breath value which isbelow a breadth threshold score. The non-transitory machine-readablemedium providing instructions that further causes the machine toidentify an inventor as an innovator when the inventor has a depth scoreabove a threshold depth score and a breadth value above a breadththreshold score. The above non-transitory machine-readable mediumproviding instructions that further causes the machine to identify aninventor as an architect when the inventor has a depth score above athreshold depth score and a breadth value above a breadth thresholdscore, and the inventor has co-inventors with different specialty areas.The above non-transitory machine-readable medium providing instructionsthat further causes the machine to: review one or more co-Inventors on acollection of patent documents on which the inventor is listed: and rankthe inventor based on the number of unique one or more co-inventorsassociated with the inventor on the collection of patent documents. Theabove non-transitory machine-readable medium providing instructions thatfurther causes the machine to identify an inventor as an innovator whenthe inventor has a depth score above a threshold depth score and abreadth value above a breadth threshold score, and with co-inventorsthat change above a selected frequency.

A non-transitory machine-readable medium providing instructions that,when executed by a machine, cause the machine to perform operations toforecast patent value comprising: determining a forward citation scorefor a first inventor of a patent document based on citations to otherpatents associated with the first inventor; and determining a forwardcitation score for a second inventor of the patent document based oncitations to other patents associated with the second inventor; andforecasting the forward citation of the patent based on the determinedforward citation score for the first inventor and the determined forwardcitation score for the second inventor. The above non-transitorymachine-readable medium providing instructions that further causes themachine to average the forward citation score for the first inventor andthe forward citation score for the second inventor to arrive at a numberto predict the forward citation score of the patent document. The abovenon-transitory machine-readable medium providing instructions thatfurther causes the machine to value the patent document in response tothe predicted number for the forward citation score of the patentdocument.

A non-transitory machine-readable medium providing instructions that,when executed by a machine, cause the machine to perform operations toforecast patent value by; predicting a value of a patent that includes aplurality of inventors by: identifying a first inventor as a specialisthaving a high depth and low breadth on a number of patents in which thefirst inventor has previously been named as an inventor; determining theactivity level of the first inventor; identifying a second inventor as aspecialist having a high depth and low breadth on a number of patents inwhich the second inventor has previously been named as an inventor;determining the activity level of the second inventor; and averaging theactivity level of the first inventor and the activity level of thesecond inventor; and predicting the value of the patent based on theaverage activity number. The above non-transitory machine-readablemedium providing instructions that further causes the machine to:identify a third inventor as an architect having a high depth and highbreadth on a number of patents in which the third inventor haspreviously been named as an inventor; determine the activity level ofthe third inventor; identify a fourth inventor as an architect having ahigh depth and high breadth on a number of patents in which the fourthinventor has previously been named as an inventor; determine theactivity level of the fourth inventor; and average the activity level ofthe third inventor and the activity level of the fourth inventor; andpredict the value of the patent based on the average activity number forspecialists and the average activity number for architects.

A non-transitory machine-readable medium providing instructions that,when executed by a machine, cause the machine to perform operations toforecast patent value by; identifying a set of inventors associated withan assignee company: identifying the inventors in the set into at leastspecialists, architects, and innovators based on classifications of thepatents or patent publications on which an individual inventor is named,a specialist having associated with the invention a particular inventorhaving a depth above a threshold and a breadth below a breadththreshold, an architect having a depth above the depth threshold and abreadth above a breadth threshold, and an innovator having high depthand high breadth and with co-inventors that change above a selectedfrequency; and identifying an event; monitoring the level of change inactivity levels of the specialists in the set, the innovators in theset, and the architects in the set to determine the effect of the event.The above non-transitory machine-readable medium providing instructionsthat further causes the machine to monitor the value of an organizationwhen the event is an event associated with the organization. The abovenon-transitory machine-readable medium providing instructions thatfurther causes the machine to determine a trend in activity levels afterthe event, and associating an increased value with the organization inresponse to an upward trend in increased levels of activity of thespecialists in the set, the innovators in the set, and the architects inthe set. The above non-transitory machine-readable medium providinginstructions that further causes the machine to: normalize architectscores, specialist scores, and innovator scores over time; and plot thenormalized scores against an organizations enterprise value and marketcap; and note up and down trends after certain events, and use the upand down trends to forecast an upswing or a decline in growth of theorganization. The above non-transitory machine-readable medium providinginstructions that further causes the machine to: associate an innovationscore with the innovators; associate an architect score with thearchitects; and associate an specialist score with the specialists in anorganization; and rank the innovators, architects and specialists in anorganization based on the respective scores for the individuals in thesecategories.

A non-transitory machine-readable medium providing instructions that,when executed by a machine, cause the machine to perform operations tocompare the relative innovation between organizations by: selectingcriteria for identifying a first set of patent documents from a firstorganization and identifying a second set of patent documents from asecond organization; identifying first organization innovators in thefirst organization based on patent classifications of patents within theset that the inventors are named on, the innovators identified by usingclassification data associated with the inventions and reviewingcharacteristics of co-inventors named on patent documents they werenamed on; identifying second organization innovators in the secondorganization based on patent classifications of patents within the setthat the inventors are named on; and comparing the number of innovatorsin the first organization with the number of innovators in the secondorganization. The above non-transitory machine-readable medium providinginstructions that further causes the machine to normalize the numbers ofinnovators. The above non-transitory machine-readable medium providinginstructions that further causes the machine to select the criteria foridentifying a first set of patent documents from the first organizationand to identify a second set of patent documents from the secondorganization includes a first time period and a second time period. Theabove non-transitory machine-readable medium providing instructions thatfurther causes the machine to select the criteria for identifying afirst set of patent documents from the first organization and foridentifying a second set of patent documents from the secondorganization includes a specific field as depicted by a patentclassification. The above non-transitory machine-readable mediumproviding instructions that further causes the machine to: searchdatabases for changes in status of at least one of the innovators in thegroup of innovators in one of a first organization or a secondorganization; and recalculate an innovation score for the innovators inthe at least one organization. The above non-transitory machine-readablemedium providing instructions that further causes the machine to:Identify a first organization architects in the first organization basedon patent classifications of patents within the set that the inventorsare named on, the architects identified by using classification dataassociated with the inventions and reviewing characteristics ofco-inventors named on patent documents they were named on; and Identifya second organization innovators in the second organization based onpatent classifications of patents within the set that the inventors arenamed on. The above non-transitory machine-readable medium providinginstructions that further causes the machine to select criteria for thesubset of patents that includes a plurality of patent classifications.The above computer system wherein scores from at least one of the depthscore calculating module or the breadth score calculating module areused to evaluate an inventor's capability for generating high-valuepatents. The above computer system further comprising an input from apatent classification system for one or more patents associated with aninventor. The above computer system wherein the patent classificationsystem includes plurality of classifications with a first level and asecond level, at least one of the depth score or the breadth score arecalculated from at least the first level classification and the secondlevel classification.

The above computer system further comprising a forward citationprediction module for calculating a forward citation predictionindicator for an inventor. The above computer system wherein scores aredetermined for a plurality of inventors, the scores used to comparevarious inventors. The above computer system further comprising aclassification module. The classification module identifying an inventoras a specialist when the inventor has a depth score above a thresholddepth score and a breath value which is below a breadth threshold score.The above computer system wherein the classification module identifiesan inventor as an innovator when the inventor has a depth score above athreshold depth score and a breadth value above a breadth thresholdscore. The above computer system wherein the classification moduleidentifies an inventor as an architect when the inventor has a depthscore above a threshold depth score and a breadth value above a breadththreshold score, and the inventor has co-inventors with differentspecialty areas. The above computer system wherein the classificationmodule identifies an inventor as an innovator when the inventor has adepth score above a threshold depth score and a breadth value above abreadth threshold score, and with co-inventors that change above aselected frequency.

A computer system for forecasting patent value comprising: amicroprocessor; a memory communicatively coupled to the microprocessor;a forward citation score module communicatively coupled to at least themicroprocessor, that determines a forward citation score for a firstinventor of a patent document based on citations to other patentsassociated with the first inventor; and determines a forward citationscore for a second inventor of the patent document based on citations toother patents associated with the second inventor; and forecasts theforward citation of the patent based on the determined forward citationscore for the first inventor and the determined forward citation scorefor the second inventor. The above computer system for forecastingpatent value further a forward citation prediction module that averagesthe forward citation score for the first inventor and the forwardcitation score for the second inventor to arrive at a number to predictthe forward citation score of the patent document produced by the firstinventor and the second inventor.

A computer system for predicting a value of a patent that includes aplurality of inventors comprising: a classification module foridentifying a first inventor as a specialist having a high depth and lowbreadth on a number of patents in which the first inventor haspreviously been named as an inventor; and identifying a second inventoras a specialist having a high depth and low breadth on a number ofpatents in which the second inventor has previously been named as aninventor; an inventive activity module for determining the activitylevel of the first inventor; and determining the activity level of thesecond inventor; and averaging the activity level of the first inventorand the activity level of the second inventor; and a prediction modulefor predicting the value of the patent based on the average activitynumber. The above computer system for predicting a value of a patentwherein the classification module identifying a third inventor as anarchitect having a high depth and high breadth on a number of patents inwhich the third inventor has previously been named as an inventor;identifying a fourth inventor as an architect having a high depth andhigh breadth on a number of patents in which the fourth inventor haspreviously been named as an inventor; and wherein the inventive activitymodule determines the activity level of the third inventor; anddetermines the activity level of the fourth inventor; and averages theactivity level of the third inventor and the activity level of thefourth inventor; and wherein the prediction module for predicts thevalue of the patent based on the average activity number for specialistsand the average activity number for architects. The above computersystem for predicting a value of a patent wherein the classificationmodule identifies a fifth inventor as an innovator having a high depthand high breadth on a number of patents in which the fifth inventor haspreviously been named as an inventor; identifies a sixth inventor as aninnovator having a high depth and high breadth on a number of patents inwhich the sixth inventor has previously been named as an inventor;determines the activity level of the fifth inventor; determines theactivity level of the sixth inventor; and averages the activity level ofthe fifth inventor and the activity level of the sixth inventor; andwherein the prediction module predicts the value of the patent based onthe average activity number for specialists and the average activitynumber for innovators.

A computer system for monitoring the value of an organizationcomprising: a microprocessor; a memory communicatively coupled to themicroprocessor; an identification module for identifying a set ofinventors associated with an assignee company, the identification modulealso identifying at least one event; a classification module foridentifying the inventors in the set into at least specialists,architects, and innovators based on classifications of the patents orpatent publications on which an individual inventor is named, aspecialist having associated with the invention a particular inventorhaving a depth above a threshold and a breadth below a breadththreshold, an architect having a depth above the depth threshold and abreadth above a breadth threshold, and an innovator having high depthand high breadth and with co-inventors that change above a selectedfrequency; and a monitor for monitoring activity levels for specialists,innovators and architects, the monitor monitoring the level of change inactivity levels of the specialists in the set, the innovators in theset, and the architects in the set to determine the effect of the event.The above computer system for monitoring the value of an organizationfurther comprising: a statistical module for normalizing architectscores, specialist scores, and innovator scores over time; and plottingthe normalized scores against an organizations enterprise value andmarket cap; and noting up and down trends after certain events, andusing the up and down trends to forecast an upswing or a decline ingrowth of the organization.

A computer system for comparing the relative innovation betweenorganizations comprising: a selection module for selecting criteria foridentifying a first set of patent documents from a first organizationand identifying a second set of patent documents from a secondorganization; an identification module for identifying firstorganization innovators in the first organization based on patentclassifications of patents within the set that the inventors are namedon, the innovators identified by using patent classification dataassociated with the inventions and reviewing characteristics ofco-inventors named on patent documents they were named on; theidentification module also identifying second organization innovators inthe second organization based on patent classifications of patentswithin the set that the inventors are named on; and a compare module forcomparing the number of innovators in the first organization with thenumber of innovators in the second organization. The above computersystem for comparing the relative innovation between organizationsfurther comprising a statistical module which normalizes the numbers ofinnovators for the first organization and the second organization. Theabove computer system for comparing the relative innovation betweenorganizations wherein the selection module selects the criteria foridentifying a first set of patent documents from the first organizationand for identifying a second set of patent documents from the secondorganization includes a first time period and a second time period. Theabove computer system for comparing the relative innovation betweenorganizations wherein the selection module selects the criteria foridentifying a first set of patent documents from the first organizationand for identifying a second set of patent documents from the secondorganization includes a specific field as depicted by a patentclassification of a governmental patent body. The above computer systemfor comparing the relative innovation between organizations wherein theselection module searches databases for changes in status of at leastone of the innovators in the group of innovators in one of a firstorganization or a second organization; and the microprocessorrecalculates an innovation score for the innovators in the at least oneorganization in response to finding a change in status of at least oneinnovator, the microprocessor recalculation the innovation score for theorganization in which the change of status was found. The above computersystem for comparing the relative innovation between organizationswherein the identification module Identifies first organizationarchitects in the first organization based on patent classifications ofpatents within the set that the inventors are named on, the architectsidentified by using classification data associated with the inventionsand reviewing characteristics of co-inventors named on patent documentsthey were named on; and Identifies second organization innovators in thesecond organization based on patent classifications of patents withinthe set that the inventors are named on. The above computer system forcomparing the relative innovation between organizations wherein theselection criteria for the subset of patents includes a plurality ofpublic patent classifications. The above computer system for comparingthe relative innovation between organizations wherein the selectioncriteria for the subset of patents includes a plurality of internationalpatent classification codes.

A method for determining the technological ability of an inventorcomprising: determining a set of inventions invented by the inventor;calculating a depth score for the inventor using a publically availablepatent classification scheme for identifying patents, the depth scorebeing higher when the classification of each of the patents of the setof patents is for a similar technology; calculating a breadth score forthe inventor using a publically available patent classification schemefor identifying patents, the breadth score being higher when theclassifications of the patents in the set of inventions is for differenttechnologies. The above method wherein the inventor's capability forgenerating high-value patents is ranked based on at least one of thedepth score or the breadth scores. The above method further comprisinganalyzing the depth score and the breadth score to characterize theinventor. The above method further comprising analyzing the depth scoreand the breadth score to characterize the inventor as an innovator. Theabove method further comprising analyzing the depth score and thebreadth score to characterize the inventor as an architect. The abovemethod further comprising analyzing the depth score and the breadthscore to characterize the inventor as a specialist. The above methodfurther comprising analyzing depth and breadth scores of co-Inventors ofthe inventions within the set of inventions associated with theinventor. The above method wherein the inventor is provided with acollaboration score based on the depth and breadth score of theco-inventors. The above method wherein the number of forward citationsto a patent can be predicted based, at least in part on the depth scoreand breadth score of the inventor. The above method wherein thepredicted number of forward citations is used to predict the strength ofa patent the inventor is named on. The above method wherein the numberof forward citations to a patent can be predicted based, at least inpart on the depth score and breadth score of the inventor, and at leastin part, on the depth score and breadth score of co-inventors of thepatent. The above method wherein the predicted number of forwardcitations is used to predict the strength of a patent the inventor andco-inventor are named on.

A method for comparing the technological ability of a first inventor tothe technological ability of a second inventor comprising: determining afirst set of inventions invented by a first inventor; determining asecond set of inventions invented by a second inventor; calculating adepth score for a first inventor using a publically available patentclassification scheme for identifying patents; calculating a breadthscore for the first inventor using a publically available patentclassification scheme for classifying patents; calculating a depth scorefor a second inventor using a publically available patent classificationscheme for classifying patents; calculating a breadth score for thesecond inventor using a publically available patent classificationscheme for classifying patents; and ranking the first inventor relativeto second inventor based on at least one of the depth scores and thebreadth scores. The above method wherein a patent is classified using aplurality of patent classifications, the patent classificationsincluding a first level patent classification and a second level patentclassification, at least one of the depth score or the breadth scorescalculated from at least the first level patent classification and thesecond level patent classifications. The above method further comprisinganalysing the depth score and the breadth score to characterize thefirst inventor and the second inventor. The above method furthercomprising analysing the depth score and the breadth score tocharacterize at least one of the first inventor and the second inventoras an innovator. The above method further comprising analysing the depthscore and the breadth score to characterize at least one of the firstinventor and the second inventor as an architect. The above methodfurther comprising analyzing the depth score and the breadth score tocharacterize at least one of the first inventor and the second inventoras a specialist.

A method for determining the health of an organization comprising:determining a first set of inventions invented by a first set ofinventors; determining a second set of inventions invented by a secondset of inventors; determining a third set of inventions invented by athird set of inventors; calculating a depth score for a first set ofinventors using a publically available patent classification scheme forclassifying patents; calculating a breadth score for the first set ofinventors using a publically available patent classification scheme forclassifying patents; calculating a depth score for a second set ofinventors using a publically available patent classification scheme forclassifying patents; calculating a breadth score for the second set ofinventors using a publically available patent classification scheme forclassifying patents; characterizing one of the first set, second set, orthird set of inventors as innovators; characterizing one of the firstset, second set, or third set of inventors as specialists; andcharacterizing one of the first set, second set, or third set ofinventors as architects. The above method for determining the health ofan organization further comprising plotting the scores for innovators,specialists and architects over time and correlating organization eventsto changes in the scores. The above method for determining the health ofan organization further comprising plotting the scores for innovators,specialists and architects over time and correlating changes in companyvalue to changes in the scores. The above method for determining thehealth of an organization further comprising plotting the scores forinnovators, specialists and architects over time and predicting changesin company value to result from changes in the scores. The above methodfor determining the health of an organization further comprisingdetermining the sets of inventors and characterizing them form a secondcompany and comparing the first company's scores to the second company'sscores.

This has been a detailed description of some exemplary embodiments ofthe invention(s) contained within the disclosed subject matter. Suchinvention(s) may be referred to, individually and/or collectively,herein by the term “invention” merely for convenience and withoutintending to limit the scope of this application to any single inventionor inventive concept if more than one is in fact disclosed. The detaileddescription refers to the accompanying drawings that form a part hereofand which shows by way of illustration, but not of limitation, somespecific embodiments of the invention, including a preferred embodiment.These embodiments are described in sufficient detail to enable those ofordinary skill in the art to understand and implement the inventivesubject matter. Other embodiments may be utilized and changes may bemade without departing from the scope of the inventive subject matter.Thus, although specific embodiments have been illustrated and describedherein, any arrangement calculated to achieve the same purpose may besubstituted for the specific embodiments shown. This disclosure isintended to cover any and all adaptations or variations of variousembodiments. Combinations of the above embodiments, and otherembodiments not specifically described herein, will be apparent to thoseof skill in the art upon reviewing the above description.

Other embodiments will be apparent to those of skill in the art uponreviewing the above description. The scope of the invention should,therefore, be determined with reference to the appended claims, alongwith the full scope of equivalents to which such claims are entitled. Inthe appended claims, the terms “including” and “in which” are used asthe plain-English equivalents of the respective terms “comprising” and“wherein.” Also, in the following claims, the terms “including” and“comprising” are open-ended, that is, a system, device, article, orprocess that includes elements in addition to those listed after such aterm in a claim are still deemed to fall within the scope of that claim.The term “having” if used in the claims is an open ended term. Moreover,in the following claims, the terms “first,” “second,” and “third,” etc.are used merely as labels, and are not intended to impose numericalrequirements on their objects.

The use of the term “or” in the present description should beinterpreted as a non-exclusive or unless otherwise stated.

In the foregoing Detailed Description, various features are groupedtogether in a single embodiment for the purpose of streamlining thedisclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments of the inventionrequire more features than are expressly recited in each claim. Rather,as the following claims reflect, inventive subject matter lies in lessthan all features of a single disclosed embodiment. Thus the followingclaims are hereby incorporated into the Detailed Description, with eachclaim standing on its own as a separate example embodiment.

It is emphasized that, for purposes of the United States, the Abstractis provided to comply with 36 C.F.R. § 1.62(b) requiring an Abstractthat will allow the reader to quickly ascertain the nature and gist ofthe technical disclosure. It is submitted with the understanding that itwill not be used to interpret or limit the scope or meaning of theclaims.

It is understood that the above description is intended to beillustrative, and not restrictive. Many other embodiments will beapparent to those of skill in the art upon reviewing the abovedescription. The scope of the invention should, therefore, be determinedwith reference to the appended claims, along with the full scope ofequivalents to which such claims are entitled.

1-20. (canceled)
 21. A method, comprising: receiving, by a specialpurpose computer, first inventor data and second inventor data sent overa computer network from an inventor database; calculating, by aprocessing unit of a special purpose computer, a first depth score for afirst inventor based in the first inventor data, the calculating of thefirst depth score comprising: calculating a first summation of firstoccurrences of patents obtained by the first inventor associated with acertain patent classification code; and in the calculating of the firstsummation, weighting each occurrence of the first occurrences of patentsby inventor order; calculating, by the processing unit, a first breadthscore for the first inventor based in the first inventor data;identifying, by the processing unit, first one or more co-inventors on acollection of patent documents on which the first inventor is listedbased in the first inventor data; calculating, by the processing unit, asecond depth score for a second inventor based in the second inventordata, the calculating of the second depth score comprising: calculatinga second summation of second occurrences of patents obtained by thesecond inventor associated with the certain patent classification code;and in the calculating of the second summation, weighting eachoccurrence of the second occurrences of patents by inventor order;calculating, by the processing unit, a second breadth score for thesecond inventor based in the second inventor data; identifying, by theprocessing unit, second one or more co-inventors on a collection ofpatent documents on which the second inventor is listed based in thesecond inventor data; ranking, by the processing unit, the firstinventor relative to the second inventor based on the first scores and afirst number of unique co-inventors in the first one or moreco-inventors as well as the second scores and a second number of uniqueco-inventors in the second one or more co-inventors; and sending overthe computer network, by the special purpose computer, the ranking ofthe first inventor to an assessor computer which is a general computerconnected to a display device configured to graphically display theranking to an assessor of inventors.
 22. The method of claim 21, whereinthe processing unit is a graphical processing unit (GPU).
 23. The methodof claim 22, wherein receiving of the first inventor data and the secondinventor data is by a central processing unit which manages thereceiving of the data and relays it to the GPU when the data isrequested by the GPU for a process of the GPU.
 24. The method of claim23, wherein the sending of the ranking is by the CPU, via acommunications interface, and wherein the CPU manages the sending of theranking and relays it to the communication interface to send the rankingover the computer network.
 25. The method of claim 21, wherein thespecial purpose computer, a computer hosting the inventor database, andthe assessor computer are respective peer nodes in a peer-to-peercomputer network which is at least a part of the computer network. 26.The method of claim 21, wherein the special purpose computer and acomputer hosting the inventor database are respective server nodes in aserver-client computer network which is at least a part of the computernetwork, and wherein the assessor computer is a client node in theserver-client computer network.
 27. The method of claim 21, wherein acomputer hosting the inventor database is a server node in aserver-client computer network, and wherein the special purpose computerand the assessor computer are respective client nodes in theserver-client computer network.
 28. The method of claim 21, furthercomprising calculating, by the processing unit, a first collaborationscore according to depth scores of co-inventors of the first inventor,wherein the collaboration score is reflective of an ability of the firstinventor to work with other inventors, and wherein the calculations ofthe depth scores of the co-inventors are performed in a similar way asthe calculation of the first depth score of the first inventor.
 29. Themethod of claim 21 further comprising calculating, by the processingunit, a forward citation prediction indicator for the first inventoraccording to the first depth score.
 30. The method of claim 21 furthercomprising identifying, by the processing unit, the first inventor as aspecialist when the first depth score is above a threshold depth scoreand the first breadth score is below a breadth threshold score.
 31. Themethod of claim 21 further comprising identifying, by the processingunit, the first inventor as an innovator when the first depth score isabove a threshold depth score and the first breadth score is above abreadth threshold score.
 32. The method of claim 21 further comprisingidentifying, by the processing unit, the first inventor as an architectwhen the first depth score is above a threshold depth score and thefirst breadth score is above a breadth threshold score, and the firstinventor has co-inventors with different specialty areas.
 33. The methodof claim 21 further comprising identifying, by the processing unit, thefirst inventor as an innovator when the first depth score is above athreshold depth score and the first breadth score is above a breadththreshold score, and with co-inventors that change above a selectedfrequency.
 34. The method of claim 21, wherein the calculating of thefirst breadth score comprises: calculating a third summation of thirdoccurrences of patents obtained by the first inventor associated with asecond certain patent classification code; and in the calculating of thethird summation, weighting each occurrence of the third occurrences ofpatents by inventor order.
 35. The method of claim 28, wherein thecalculating of the second breadth score comprises: calculating a fourthsummation of fourth occurrences of patents obtained by the secondinventor associated with the second certain patent classification code;and in the calculating of the fourth summation, weighting eachoccurrence of the fourth occurrences of patents by inventor order. 36.The method of claim 21 further comprising calculating, by the processingunit, a forward citation prediction indicator for the first inventoraccording to the first depth score and the second breadth score.
 37. Themethod of claim 21, wherein the calculating of the first depth scorefurther comprises: calculating a third summation of third occurrences ofpatents obtained by the first inventor associated with a second certainpatent classification code, wherein the certain patent classificationcode is included in a first classification level and the second certainpatent classification code is included in a second classification level;and in the calculating of the third summation, weighting each occurrenceof the third occurrences of patents by inventor order.
 38. The method ofclaim 31, wherein the calculating of the second depth score furthercomprises: calculating a fourth summation of fourth occurrences ofpatents obtained by the second inventor associated with the secondcertain patent classification code; and in the calculating of the fourthsummation, weighting each occurrence of the fourth occurrences ofpatents by inventor order.
 39. A special purpose computer comprisingmemory comprising an instruction set executable by a central processingunit of the special purpose computer that when executed by theprocessing unit causes the special purpose computer to perform a methodcomprising the following steps: receiving first inventor data and secondinventor data sent over a computer network from an inventor database;calculating a first depth score for a first inventor based in the firstinventor data, the calculating of the first depth score comprising:calculating a first summation of first occurrences of patents obtainedby the first inventor associated with a certain patent classificationcode; and in the calculating of the first summation, weighting eachoccurrence of the first occurrences of patents by inventor order;calculating a first breadth score for the first inventor based in thefirst inventor data; identifying first one or more co-inventors on acollection of patent documents on which the first inventor is listedbased in the first inventor data; calculating a second depth score for asecond inventor based in the second inventor data, the calculating ofthe second depth score comprising: calculating a second summation ofsecond occurrences of patents obtained by the second inventor associatedwith the certain patent classification code; and in the calculating ofthe second summation, weighting each occurrence of the secondoccurrences of patents by inventor order; calculating a second breadthscore for the second inventor based in the second inventor data;identifying second one or more co-inventors on a collection of patentdocuments on which the second inventor is listed based in the secondinventor data; ranking he first inventor relative to the second inventorbased on the first scores and a first number of unique co-inventors inthe first one or more co-inventors as well as the second scores and asecond number of unique co-inventors in the second one or moreco-inventors; and sending over the computer network, via acommunications interface, the ranking of the first inventor to anassessor computer which is a general computer connected to a displaydevice configured to graphically display the ranking to an assessor ofinventors.
 40. A system, comprising: a special purpose computer; andmemory comprising an instruction set executable by a central processingunit (CPU) of the special purpose computer to cause the special purposecomputer to: receive first inventor data and second inventor data sentover a computer network from an inventor database; calculate, via agraphical processing unit (GPU), a first depth score for a firstinventor based in the first inventor data, the calculating of the firstdepth score comprising: calculating a first summation of firstoccurrences of patents obtained by the first inventor associated with acertain patent classification code; and in the calculating of the firstsummation, weighting each occurrence of the first occurrences of patentsby inventor order; calculate, via the GPU, a first breadth score for thefirst inventor based in the first inventor data; identify, via the GPU,first one or more co-inventors on a collection of patent documents onwhich the first inventor is listed based in the first inventor data;calculate, via the GPU, a second depth score for a second inventor basedin the second inventor data, the calculating of the second depth scorecomprising: calculating a second summation of second occurrences ofpatents obtained by the second inventor associated with the certainpatent classification code; and in the calculating of the secondsummation, weighting each occurrence of the second occurrences ofpatents by inventor order; calculate, via the GPU, a second breadthscore for the first inventor based in the second inventor data;identify, via the GPU, second one or more co-inventors on a collectionof patent documents on which the second inventor is listed based in thesecond inventor data; rank, via the GPU, the first inventor relative tothe second inventor based on the first scores and a first number ofunique co-inventors in the first one or more co-inventors as well as thesecond scores and a second number of unique co-inventors in the secondone or more co-inventors; and send over the computer network, via acommunications interface, the ranking of the first inventor to anassessor computer which is a general computer connected to a displaydevice configured to graphically display the ranking to an assessor ofinventors.